Fall detection-based help-seeking method and electronic device

ABSTRACT

A fall detection-based help-seeking method and an electronic device, to improve accuracy of fall detection performed by an electronic device and reduce a probability of mistakenly triggering automatic help-seeking of the electronic device. A solution includes: the electronic device includes a motion sensor and the motion sensor includes an acceleration sensor or a gyro sensor. The electronic device collects a first motion parameter of a user by using the motion sensor. The electronic device obtains a fall confidence of the first motion parameter if the first motion parameter matches a first preset fall parameter, where the fall confidence of the first motion parameter is used to represent a probability that the first motion parameter is a motion parameter collected when the user falls. The electronic device sends help-seeking information if the fall confidence of the first motion parameter is greater than a preset confidence threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation of International Patent Application No.PCT/CN2020/098359, filed on Jun. 28, 2020, which claims priority toChinese Patent Application No. 201910577799.3, filed on Jun. 28, 2019.The disclosures of the aforementioned applications are herebyincorporated by reference in their entireties.

TECHNICAL FIELD

Embodiments relate to the field of wearable technologies, and inparticular, to a fall detection-based help-seeking method and anelectronic device.

BACKGROUND

With the development of society, more and more people live alone. Forexample, as aging intensifies, there are more empty-nesters. For peopleliving alone, timely medical aid after an accidental fall caneffectively reduce a risk of accidental injury or death. Therefore, itis of great practical significance to automatically detect an accidentalfall and send help-seeking information.

Currently, some electronic devices (such as a smartwatch) have afunction of automatically detecting a fall and sending help-seekinginformation. Users (such as elderly people) wearing such electronicdevices can be aided in a timely manner after accidental falls.Specifically, these electronic devices each include a motion sensor(such as an acceleration sensor or a gyro sensor) configured to detect amotion parameter of a user. The electronic device may collect the motionparameter of the user by using the motion sensor, and automatically sendhelp-seeking information (for example, automatically make a help-seekingcall or automatically play a help-seeking speech) when the collectedmotion parameter matches a preset fall parameter.

However, a motion parameter of a common action (such as jumping) of theuser is similar to a motion parameter of a fall. Therefore, when theuser performs a common action whose motion parameter is similar to themotion parameter of the fall, the electronic device also considers thata fall event occurs to the user, and automatically sends help-seekinginformation. Accuracy of fall detection performed by the electronicdevice is relatively low, and automatic help-seeking is likely to betriggered by mistake.

SUMMARY

Embodiments provide a fall detection-based help-seeking method and anelectronic device to improve accuracy of fall detection performed by anelectronic device and reduce a probability of mistakenly triggeringautomatic help-seeking of the electronic device.

To achieve the foregoing objectives, the following solutions are used inthe embodiments.

According to a first aspect, an embodiment provides a falldetection-based help-seeking method, and the method may be applied to anelectronic device. The electronic device includes a motion sensor, andthe motion sensor includes an acceleration sensor or a gyro sensor. Themethod may include: the electronic device collects a first motionparameter of a user by using the motion sensor. The electronic deviceobtains a fall confidence of the first motion parameter if the firstmotion parameter matches a first preset fall parameter. The electronicdevice sends help-seeking information if the fall confidence of thefirst motion parameter is greater than a preset confidence threshold.The fall confidence of the first motion parameter is used to represent aprobability that the first motion parameter is a motion parametercollected when the user falls.

In this embodiment, that the electronic device determines whether thefirst motion parameter matches the first preset fall parameter isreferred to as “first-layer detection” of fall detection.

In this embodiment, after determining that the first motion parametermatches the first preset fall parameter (that is, determining that theuser may fall), the electronic device may further determine whether thefall confidence is greater than the preset confidence threshold. Inother words, the electronic device may determine, through doubledetection, whether the user falls. In this way, accuracy of falldetection performed by the electronic device can be improved, and aprobability of mistakenly triggering automatic help-seeking of theelectronic device can be reduced.

With reference to the first aspect, in a possible implementation, amethod in which the electronic device sends the help-seeking informationmay include: the electronic device plays a help-seeking speech or analarm sound. After determining that the user falls, the electronicdevice plays the help-seeking speech or the alarm sound. In this way,people around can find the fallen user in a timely manner and aid theuser in a timely manner.

With reference to the first aspect, in another possible implementation,a method in which the electronic device sends the help-seekinginformation may include: the electronic device calls a first presetcontact. The first preset contact is any emergency contact or a publicrescue service preset in the electronic device. For example, a phonenumber of the public rescue service may be an emergency phone number(for example, 120) or an alarm phone number (for example, 110). Theemergency contact may be preset by the user in a wearable device 10.

With reference to the first aspect, in another possible implementation,if the user is not seriously injured after falling, the user has anautonomous behavior capability. In this case, the user may expect toautonomously select an object for help-seeking. For example, when thefall is not serious, the user may prefer to seek help from a familymember or a friend rather than dialing the emergency phone number 120.Based on this case, before sending the help-seeking information, theelectronic device may display a first interface including a plurality ofcontact options, where each contact option corresponds to one presetcontact in the electronic device. The electronic device may receive aselection operation performed by the user on a contact option of thefirst preset contact in the first interface. The electronic device maycall the first preset contact in response to the selection operationperformed by the user on the contact option of the first preset contact.

With reference to the first aspect, in another possible implementation,after the electronic device calls the preset contact corresponding tothe contact option selected by the user (that is, requests voicecommunication with the contact selected by the user), if the voicecommunication is not answered within a first preset time period (forexample, 1 minute, 50 seconds, 30 seconds, or 15 seconds), theelectronic device may automatically call another preset contact, to sendthe help-seeking information. In some other embodiments, if the voicecommunication is not answered within the first preset time period, theelectronic device may further automatically send a first message to oneor more preset contacts.

With reference to the first aspect, in another possible implementation,if the electronic device does not receive a selection operation of theuser in the first interface within a second preset time period, theelectronic device may automatically call any preset contact, or send afirst message to one or more preset contacts.

With reference to the first aspect, in another possible implementation,a method in which the electronic device sends the help-seekinginformation may include: the electronic device sends a first message toone or more preset contacts through one or more communicationapplications. The first message includes the help-seeking information.The preset contact includes the emergency contact or the public rescueservice preset in the electronic device. For example, the electronicdevice may send the first message to the one or more preset contactsthrough the one or more communication applications. The one or morecommunication applications may be communication applications installedin the electronic device. The one or more communication applications areapplications that are installed in the electronic device and that cancommunicate with another device (for example, a mobile phone of a presetcontact). For example, the communication application may be a messagingapplication, Email, iMessage, WeChat, QQ, or Alipay, or the like.

With reference to the first aspect, in another possible implementation,to enable a rescuer (for example, a family member or a friend of theuser, or a public rescue service worker) to accurately find the user ina timely manner, the electronic device may further include a positioningmodule. The method may further include: the electronic device obtainsgeographical location information of the electronic device by using thepositioning module. The first message further includes the geographicallocation information.

With reference to the first aspect, in another possible implementation,after sending the first message to the one or more preset contacts, theelectronic device may send first prompt information. The first promptinformation is used to prompt the user that the electronic device hassent a help-seeking message.

With reference to the first aspect, in another possible implementation,if the user is seriously injured after falling, the user needs to beaided in a timely manner, to effectively reduce a risk of accidentalinjury or death. However, after the electronic device makes the call orsends the first message for help-seeking, even if the rescuer canreceive the help-seeking of the user in a timely manner, the rescuer maynot be able to aid the user in a timely manner. In this case, because anoptimal aid time is missed, life of the user may be in danger, or a bodyof the user may be irreversibly harmed. To increase a probability thatthe user is aided in a timely manner after the user falls, theelectronic device may further play the help-seeking speech or the alarmsound when making the call and/or sending the first message. In thisway, after the user falls, people around can find the fallen user in atimely manner and aid the user in a timely manner.

With reference to the first aspect, in another possible implementation,the electronic device may further include a positioning module. Themethod may further include: the electronic device collects speech dataof the user; and in response to the speech data, performs a speechcontrol event corresponding to the speech data, and sends thehelp-seeking information. For example, the speech data may be “call myson”, “send a WeChat message to tell my daughter that I fell”, or “dial120”.

With reference to the first aspect, in another possible implementation,the electronic device stores model code of a first fall detection model.The first fall detection model is used to determine a fall confidence ofa motion parameter. The first fall detection model is an artificialintelligence (AI) model obtained by performing sample training by usinga plurality of second motion parameters. Alternatively, the first falldetection model is an AI model obtained by performing sample training byusing a plurality of second motion parameters and a plurality of thirdmotion parameters. The plurality of second motion parameters are motionparameters collected when a plurality of users fall. The plurality ofthird motion parameters are motion parameters collected when theplurality of users perform a preset interference action.Correspondingly, that the electronic device obtains the fall confidenceof the first motion parameter may include: the electronic device runsthe model code of the first fall detection model, to determine the fallconfidence of the first motion parameter. In this embodiment, falldetection performed by comparing the fall confidence obtained by runningthe model code of the first fall detection model with the presetconfidence threshold is referred to as “third-layer detection”. It maybe understood that, compared with “second-layer detection”, the“third-layer detection” is more accurate, and therefore, accuracy offall detection can be improved.

With reference to the first aspect, in another possible implementation,that the electronic device obtains the fall confidence of the firstmotion parameter includes: the electronic device obtains a matchingdegree between the first motion parameter and a first presetinterference parameter, and determines the fall confidence based on thematching degree. A lower matching degree indicates a higher fallconfidence, and a higher matching degree indicates a lower fallconfidence. In this embodiment, “the electronic device compares thefirst motion parameter with the first preset interference parameter” isreferred to as “second-layer detection”. Through the “second-layerdetection”, the electronic device can exclude mistaken triggering causedby the preset interference action on automatic help-seeking of theelectronic device.

With reference to the first aspect, in another possible implementation,to further improve accuracy of fall detection performed by the wearabledevice 10, in some other embodiments, the electronic device maydetermine, through triple detection, that is, the “first-layerdetection”, the “second-layer detection”, and the “third-layerdetection”, whether the user falls. For example, if the first motionparameter matches the first preset fall parameter, before the electronicdevice obtains the fall confidence of the first motion parameter, themethod may further include: the electronic device determines that thefirst motion parameter is not a first preset interference parameter.

With reference to the first aspect, in another possible implementation,the electronic device may be a first wearable device. A type of thefirst wearable device is at least one of a wearable device supported bya wrist, a wearable device supported by a head, and a wearable devicesupported by a foot.

With reference to the first aspect, in another possible implementation,the method may further include: the electronic device receives the modelcode that is of the first fall detection model and that is sent by aserver. The electronic device stores the model code of the first falldetection model.

The server stores model code of a plurality of fall detection models.Each fall detection model corresponds to one type of wearable devices.Different types of electronic devices (such as wearable devices)correspond to different fall detection models. The first fall detectionmodel is a fall detection model corresponding to the type of the firstwearable device.

With reference to the first aspect, in another possible implementation,the method may further include: the electronic device receives the firstpreset fall parameter sent by the server. The electronic device storesthe first preset fall parameter. The server stores a plurality of presetfall parameters, each preset fall parameter corresponds to one type ofwearable devices, different types of wearable devices correspond todifferent preset fall parameters, and the first preset fall parameter isa preset fall parameter corresponding to the type of the first wearabledevice.

With reference to the first aspect, in another possible implementation,the method may further include: the electronic device receives the firstpreset interference parameter sent by the server. The electronic devicestores the first preset interference parameter. The server stores aplurality of preset interference parameters, each preset interferenceparameter corresponds to one type of wearable devices, different typesof wearable devices correspond to different preset interferenceparameters, and the first preset interference parameter is a presetinterference parameter corresponding to the type of the first wearabledevice.

With reference to the first aspect, in another possible implementation,the method may further include: the electronic device sends a secondmessage to the server if the fall confidence of the first motionparameter is greater than the preset confidence threshold. The secondmessage includes the first motion parameter, first indicationinformation, and a first identifier. The first indication information isused to indicate that the first motion parameter is a motion parametercollected when the user falls. The first identifier is used to indicatea type of the electronic device (for example, the first wearabledevice). The second message is used to indicate the server to update thefirst preset fall parameter and the first fall detection model by usingthe first motion parameter.

It may be understood that the server may receive motion parameters sentby a large quantity of electronic devices after the electronic devicesdetermine that users fall and update the first preset fall parameter andthe first fall detection model in the server by using the motionparameters. That the server updates the first fall detection model byusing the first motion parameter as a fall parameter means that theserver performs model training by using the first motion parameter as atraining sample, so that the first fall detection model can learn of acapability of determining that the first motion parameter is motion datacollected when the user falls. Then, the server may further send anupdated first preset fall parameter and model code of an updated firstfall detection model to a plurality of electronic devices managed by theserver. For example, the server may periodically send the updated firstpreset fall parameter and the model code of the updated first falldetection model to the plurality of electronic devices. The electronicdevice performs fall detection by using the updated first preset fallparameter and the model code of the updated first fall detection model,so that accuracy of fall detection can be improved.

With reference to the first aspect, in another possible implementation,the method may further include: the electronic device sends a thirdmessage to the server if the fall confidence of the first motionparameter is less than or equal to the preset confidence threshold. Thethird message includes the first motion parameter, second indicationinformation, and a first identifier. The second indication informationis used to indicate that the first motion parameter is a motionparameter collected when the user performs the preset interferenceaction. The first identifier is used to indicate a type of theelectronic device (for example, the first wearable device). The thirdmessage is used to indicate the server to update the first presetinterference parameter and the first fall detection model by using thefirst motion parameter.

It may be understood that the server may receive motion parameters sentby a large quantity of electronic devices after the electronic devicesdetermine that users do not fall and update the first presetinterference parameter in the server by using the motion parameters.Then, the server may further send an updated first preset interferenceparameter to a plurality of electronic devices managed by the server.For example, the server may periodically send the updated first presetinterference parameter to the plurality of electronic devices. Theelectronic device performs fall detection by using the updated firstpreset interference parameter, so that accuracy of fall detection can beimproved.

With reference to the first aspect, in another possible implementation,the electronic device further includes a heart rate sensor and amicrophone. The method further includes: the electronic device collectsheart rate information of the user by using the heart rate sensor andcollects speech data of the user by using the microphone. If the fallconfidence of the first motion parameter is less than or equal to theconfidence threshold, the electronic device determines that themicrophone collects a preset moaning sound, bumping sound, or cryingsound, or the heart rate information indicates that a heart rate of theuser is less than a first quantity of heartbeats or greater than asecond quantity of heartbeats. The electronic device sends thehelp-seeking information. The first quantity of heartbeats is a minimumquantity of heartbeats per minute of a normal person, and the secondquantity of heartbeats is a maximum quantity of heartbeats per minute ofa normal person.

In this embodiment, the electronic device may further determine, basedon whether the user makes the preset moaning sound or crying sound andwhether the heart rate of the user is normal, whether the user falls.This can improve accuracy of fall detection performed by the electronicdevice.

With reference to the first aspect, in another possible implementation,the acceleration sensor is a 3-axis acceleration sensor, a 6-axisacceleration sensor, or a 9-axis acceleration sensor. The gyro sensor isa 3-axis gyro sensor, a 6-axis gyro sensor, or a 9-axis gyro sensor.

According to a second aspect, the embodiments provide an electronicdevice. The electronic device includes a motion sensor, and the motionsensor includes an acceleration sensor or a gyro sensor. The electronicdevice further includes a memory and one or more processors. The motionsensor, the memory, and the processor are coupled. The memory isconfigured to store computer program code, the computer program codeincludes computer instructions, and when the processor executes thecomputer instructions, the electronic device performs the methodaccording to any one of the first aspect and the possibleimplementations of the first aspect.

According to a third aspect, the embodiments provide a chip system. Thechip system is used in an electronic device including a touchscreen, andthe chip system includes one or more interface circuits and one or moreprocessors. The interface circuit and the processor are interconnectedthrough a line, the interface circuit is configured to receive a signalfrom a memory of the electronic device, and send the signal to theprocessor, and the signal includes computer instructions stored in thememory. When the processor executes the computer instructions, theelectronic device performs the method according to any one of the firstaspect and the possible implementations of the first aspect.

According to a fourth aspect, the embodiments provide a computer storagemedium. The computer storage medium includes computer instructions, andwhen the computer instructions are run on an electronic device, theelectronic device is enabled to perform the method according to any oneof the first aspect and the possible implementations of the firstaspect.

According to a fifth aspect, the embodiments provide a computer programproduct. When the computer program product runs on a computer, thecomputer is enabled to perform the method according to any one of thefirst aspect and the possible implementations of the first aspect.

It may be understood that for beneficial effects that can be achieved bythe electronic device according to the second aspect, the chip systemaccording to the third aspect, the computer storage medium according tothe fourth aspect, and the computer program product according to thefifth aspect, refer to the beneficial effects in any one of the firstaspect and the possible implementations of the first aspect. Details arenot described herein again.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A is a schematic diagram depicting an architecture of a falldetection system according to an embodiment;

FIG. 1B is a schematic diagram depicting a hardware structure of amobile phone according to an embodiment;

FIG. 2 is a schematic diagram depicting a hardware structure of asmartwatch according to an embodiment;

FIG. 3 is a flowchart depicting a fall detection-based help-seekingmethod according to an embodiment;

FIG. 4(a) and FIG. 4(b) are a schematic diagram depicting an example ofa display interface according to an embodiment;

FIG. 5(a) to FIG. 5(d) are a schematic diagram depicting an example ofanother display interface according to an embodiment;

FIG. 6A is a schematic diagram depicting an example of another displayinterface according to an embodiment;

FIG. 6B is a schematic diagram depicting logic of sending help-seekinginformation according to an embodiment;

FIG. 7 is a flowchart depicting another fall detection-basedhelp-seeking method according to an embodiment;

FIG. 8 is a flowchart depicting another fall detection-basedhelp-seeking method according to an embodiment;

FIG. 9A and FIG. 9B are a flowchart depicting another falldetection-based help-seeking method according to an embodiment;

FIG. 10A is a flowchart depicting another fall detection-basedhelp-seeking method according to an embodiment;

FIG. 10B is a schematic diagram depicting a manner of storing presetfall parameters, preset interference parameters, and model code of falldetection models according to an embodiment;

FIG. 11 is a schematic diagram depicting another manner of storingpreset fall parameters, preset interference parameters, and model codeof fall detection models according to an embodiment;

FIG. 12 is a schematic diagram depicting another manner of storingpreset fall parameters, preset interference parameters, and model codeof fall detection models according to an embodiment;

FIG. 13(a) and FIG. 13(b) are a schematic diagram depicting an exampleof another display interface according to an embodiment; and

FIG. 14 is a schematic diagram depicting a structure of a chip systemaccording to an embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following terms “first” and “second” are merely intended for apurpose of description and shall not be understood as an indication orimplication of relative importance or implicit indication of a quantityof indicated features. Therefore, a feature limited by “first” or“second” may explicitly or implicitly include one or more such features.In the descriptions of the embodiments, unless otherwise stated, “aplurality of” means two or more than two.

FIG. 1A is a schematic diagram depicting an architecture of a falldetection system according to an embodiment. As shown in FIG. 1A, thesystem may include an electronic device 10 and a server 20. A falldetection-based help-seeking method provided in an embodiment may beapplied to the electronic device 10. The electronic device 10 includes aplurality of sensors, a processor, and a memory. The plurality ofsensors may include a motion sensor. The motion sensor may include atleast an acceleration sensor (accelerometer or G-sensor) and a gyrosensor (gyroscope or gyro-sensor). For detailed descriptions of theprocessor and the memory, refer to descriptions in the followingembodiments. Details are not described in this embodiment.

The acceleration sensor may be a 3-axis acceleration sensor, a 6-axisacceleration sensor, or a 9-axis acceleration sensor. The gyro sensormay be a 3-axis gyro sensor, a 6-axis gyro sensor, or a 9-axis gyrosensor.

In this embodiment, the electronic device 10 may collect a motionparameter (that is, a first motion parameter) of a user by using themotion sensor. Even if the first motion parameter matches a preset fallparameter, the electronic device 10 does not immediately sendhelp-seeking information. Instead, the help-seeking information is sentonly when a fall confidence of the first motion parameter is greaterthan a preset threshold.

When the first motion parameter matches the preset fall parameter, itindicates that the user may fall. The fall confidence is used torepresent a probability that the first motion parameter is a motionparameter collected when the user falls, that is, a probability that theuser falls. A higher fall confidence indicates a higher probability thatthe user falls, and a lower fall confidence indicates a lowerprobability that the user falls.

In this embodiment, after determining that the first motion parametermatches the preset fall parameter (that is, determining that the usermay fall), the electronic device 10 may further determine whether thefall confidence is greater than the preset confidence threshold. Inother words, the electronic device 10 may determine, through doubledetection, whether the user falls. In this way, accuracy of falldetection performed by the electronic device 10 can be improved, and aprobability of mistakenly triggering automatic help-seeking of theelectronic device 10 can be reduced.

The preset fall parameter and the preset confidence threshold may beconfigured in the electronic device 10 before delivery of the electronicdevice 10. Alternatively, the preset fall parameter and the presetconfidence threshold may be sent by the server 20 to the electronicdevice 10. For example, the server 20 may periodically send an updatedpreset fall parameter and/or an updated preset confidence threshold tothe electronic device 10.

For example, the electronic device 10 in this embodiment may be aportable electronic device 10 that can be carried by the user, forexample, a mobile phone or a wearable device.

For example, the wearable device in this embodiment may be a watch-typewearable device supported by a wrist, for example, a smartwatch or asmart band; a shoes-type wearable device supported by a foot, forexample, a smart anklet worn on an ankle or a wearable product worn on ashoe or sock; a glass-type wearable device supported by a head, forexample, smart glasses, a smart helmet, or a smart headband; or awearable device used as an accessory, for example, various wearableproducts such as smart clothes, a smart bag, a smart crutch, and smartjewelry.

As shown in FIG. 1B, a mobile phone (that is, a mobile phone 100) isused as an example for the foregoing electronic device. The mobile phone100 may include a processor 110, an external memory interface 120, aninternal memory 121, a universal serial bus (universal serial bus, USB)port 130, a charging management module 140, a power management module141, a battery 142, an antenna 1, an antenna 2, a mobile communicationsmodule 150, a wireless communications module 160, an audio module 170, aspeaker 170A, a receiver 170B, a microphone 170C, a headset jack 170D, asensor module 180, a button 190, a motor 191, an indicator 192, a camera193, a display 194, a subscriber identification module (subscriberidentification module, SIM) card interface 195, and the like.

The sensor module 180 may include a pressure sensor 180A, a gyro sensor180B, a barometric pressure sensor 180C, a magnetic sensor 180D, anacceleration sensor 180E, a distance sensor 180F, an optical proximitysensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, atouch sensor 180K, an ambient light sensor 180L, a bone conductionsensor 180M, a heart rate sensor 180N, and the like. Motion sensors inthe embodiments may include the acceleration sensor 180E and the gyrosensor 180B.

It may be understood that a structure shown in this embodiment does notconstitute a limitation on the mobile phone 100. In some otherembodiments, the mobile phone 100 may include more or fewer componentsthan those shown in the figure, or some components may be combined, orsome components may be split, or different component arrangements may beused. The components shown in the figure may be implemented by hardware,software, or a combination of software and hardware.

The processor 110 may include one or more processing units. For example,the processor 110 may include an application processor (AP), a modemprocessor, a graphics processing unit (GPU), an image signal processor(ISP), a controller, a memory, a video codec, a digital signal processor(DSP), a baseband processor, and/or a neural-network processing unit(NPU). Different processing units may be independent devices or may beintegrated into one or more processors.

The controller may be a nerve center and a command center of the mobilephone 100. The controller may generate an operation control signal basedon instruction operation code and a time sequence signal, to completecontrol of instruction fetching and instruction execution.

A memory may be further disposed in the processor 110 and is configuredto store instructions and data. In some embodiments, the memory in theprocessor 110 is a cache. The memory may store instructions or data justused or cyclically used by the processor 110. If the processor 110 needsto use the instructions or the data again, the processor 110 maydirectly invoke the instructions or the data from the memory. Thisavoids repeated access and reduces a waiting time of the processor 110.Therefore, system efficiency is improved. The memory may further store aBluetooth address of the mobile phone 100. In addition, the memory mayfurther store the foregoing preset fall parameter and preset confidencethreshold, and information about one or more emergency contacts that areset by a user in the mobile phone 100, for example, a phone number andan account of an instant communication application (which is alsoreferred to as an instant messaging application). The memory may furtherstore a phone number of a public rescue service, such as an emergencyphone number (for example, 120) and an alarm phone number (for example,110).

In some embodiments, the processor 110 may include one or moreinterfaces. The interface may include an inter-integrated circuit (I2C)interface, an inter-integrated circuit sound (I2S) interface, a pulsecode modulation (PCM) interface, a universal asynchronousreceiver/transmitter ( ) interface, a mobile industry processorinterface (MIPI), a general-purpose input/output (GPIO) interface, asubscriber identification module (SIM) interface, a universal serial bus(USB) port, and/or the like.

The charging management module 140 is configured to receive charginginput from a charger. The charger may be a wireless charger or a wiredcharger. The power management module 141 is configured to connect to thebattery 142, the charging management module 140, and the processor 110.The power management module 141 receives input from the battery 142and/or the charging management module 140, and supplies power to theprocessor 110, the internal memory 121, an external memory, the display194, the camera 193, the wireless communications module 160, and thelike. The power management module 141 may be further configured tomonitor parameters such as a battery capacity, a battery cycle count,and a battery health status (electric leakage or impedance). In someother embodiments, the power management module 141 may alternatively bedisposed in the processor 110. In some other embodiments, the powermanagement module 141 and the charging management module 140 mayalternatively be disposed in a same device.

A wireless communication function of the mobile phone 100 may beimplemented through the antenna 1, the antenna 2, the mobilecommunications module 150, the wireless communications module 160, amodem processor, a baseband processor, and the like. The antenna 1 andthe antenna 2 are configured to transmit and receive electromagneticwave signals. Each antenna in the mobile phone 100 may be configured tocover one or more communication bands. Different antennas may be furthermultiplexed, to improve antenna utilization.

The mobile communications module 150 may provide a wirelesscommunication solution that includes 2G/3G/4G/5G or the like and that isapplied to the mobile phone 100. The mobile communications module 150may include at least one filter, a switch, a power amplifier, a lownoise amplifier (LNA), and the like. The mobile communications module150 may receive an electromagnetic wave through the antenna 1, performprocessing such as filtering and amplification on the receivedelectromagnetic wave, and transmit the electromagnetic wave to the modemprocessor for demodulation. The mobile communications module 150 mayfurther amplify a signal modulated by the modem processor and convertthe signal into an electromagnetic wave for radiation through theantenna 1.

The modem processor may include a modulator and a demodulator. Themodulator is configured to modulate a to-be-sent low-frequency basebandsignal into a medium/high-frequency signal. The demodulator isconfigured to demodulate a received electromagnetic wave signal into alow-frequency baseband signal. Then, the demodulator transfers thelow-frequency baseband signal obtained through demodulation to thebaseband processor for processing. The low-frequency baseband signal isprocessed by the baseband processor, and then transferred to anapplication processor. The application processor outputs a sound signalthrough an audio device (which is not limited to the speaker 170A, thereceiver 170B, or the like), or displays an image or a video through thedisplay 194.

The wireless communications module 160 may provide a wirelesscommunication solution that includes a wireless local area network(wireless local area network, WLAN) (for example, a wireless fidelity(Wi-Fi) network), Bluetooth (BT), a global navigation satellite system(GNSS), frequency modulation (FM), near field communication (NFC), aninfrared (IR) technology, or the like and that is applied to the mobilephone 100. The wireless communications module 160 may be one or moredevices integrating at least one communications processing module. Thewireless communications module 160 receives an electromagnetic wavethrough the antenna 2, performs frequency modulation and filteringprocessing on an electromagnetic wave signal, and sends a processedsignal to the processor 110. The wireless communications module 160 mayfurther receive a to-be-sent signal from the processor 110, performfrequency modulation and amplification on the signal, and convert thesignal into an electromagnetic wave for radiation through the antenna 2.

In some embodiments, the antenna 1 and the mobile communications module150 in the mobile phone 100 are coupled, and the antenna 2 and thewireless communications module 160 in the mobile phone 100 are coupled,so that the mobile phone 100 can communicate with a network and anotherdevice through a wireless communications technology. The wirelesscommunications technology may include a global system for mobilecommunications (GSM), a general packet radio service (GPRS), codedivision multiple access (CDMA), wideband code division multiple access(WCDMA), time-division code division multiple access (TD-CDMA), longterm evolution (LTE), BT, a GNSS, a WLAN, NFC, FM, an IR technology,and/or the like. The GNSS may include a global positioning system (GPS),a global navigation satellite system (GLONASS), a Beidou navigationsatellite system (BDS), a quasi-zenith satellite system (QZSS), and/or asatellite based augmentation system (SBAS).

The mobile phone 100 implements a display function by using the GPU, thedisplay 194, the application processor, and the like. The GPU is amicroprocessor for image processing and is connected to the display 194and the application processor. The GPU is configured to: performmathematical and geometric calculation and render an image. Theprocessor 110 may include one or more GPUs that execute programinstructions to generate or change display information.

The display 194 is configured to display an image, a video, and thelike. The display 194 includes a display panel. The display panel may bea liquid crystal display (LCD), an organic light-emitting diode (OLED),an active-matrix organic light-emitting diode (AMOLED), a flexiblelight-emitting diode (FLED), a mini LED, a micro LED, a micro OLED, aquantum dot light-emitting diode (QLED), or the like. In someembodiments, the mobile phone 100 may include one or N displays 194,where N is a positive integer greater than 1.

The mobile phone 100 may implement a photographing function through theISP, the camera 193, the video codec, the GPU, the display 194, theapplication processor, and the like.

The ISP is configured to process data fed back by the camera 193. Forexample, during photographing, a shutter is pressed, a ray of light istransmitted to a photosensitive element of the camera through a lens,and an optical signal is converted into an electrical signal. Thephotosensitive element of the camera transmits the electrical signal tothe ISP for processing, to convert the electrical signal into a visibleimage. The ISP may further perform algorithm optimization on noise,brightness, and complexion of the image. The ISP may further optimizeparameters such as exposure and a color temperature of a photographingscenario. In some embodiments, the ISP may be disposed in the camera193.

The camera 193 is configured to capture a static image or a video. Anoptical image of an object is generated through the lens and isprojected to the photosensitive element. The photosensitive element maybe a charge coupled device (CCD) or a complementarymetal-oxide-semiconductor (CMOS) phototransistor. The photosensitiveelement converts an optical signal into an electrical signal, and thentransmits the electrical signal to the ISP to convert the electricalsignal into a digital image signal. The ISP outputs the digital imagesignal to a DSP for processing. The DSP converts the digital imagesignal into an image signal of a standard format such as RGB or YUV. Insome embodiments, the mobile phone 100 may include one or N cameras 193,where N is a positive integer greater than 1.

The digital signal processor is configured to process a digital signal.In addition to the digital image signal, the digital signal processormay further process another digital signal. For example, when the mobilephone 100 selects a frequency, the digital signal processor isconfigured to perform Fourier transform and the like on frequencyenergy.

The video codec is configured to compress or decompress a digital video.The mobile phone 100 may support one or more video codecs. In this way,the mobile phone 100 can play or record videos of a plurality of codingformats, for example, moving picture experts group (MPEG)-1, MPEG-2,MPEG-3, and MPEG-4.

The NPU is a neural-network (NN) computing processor, quickly processesinput information by referring to a structure of a biological neuralnetwork, for example, by referring to a transfer mode between humanbrain neurons, and may further continuously perform self-learning.Applications such as intelligent cognition of the mobile phone 100, forexample, image recognition, facial recognition, speech recognition, textunderstanding, and motion parameter recognition, can be implementedthrough the NPU. For example, the NPU may run model code of a falldetection model in the embodiments, and perform the foregoing motionparameter recognition service, to determine a fall confidence of amotion parameter.

The external memory interface 120 may be configured to be connected toan external memory card such as a micro SD card, to extend a storagecapability of the mobile phone 100. The external memory cardcommunicates with the processor 110 through the external memoryinterface 120, to implement a data storage function. For example, filessuch as music and videos are stored in the external memory card.

The internal memory 121 may be configured to store computer-executableprogram code, and the executable program code includes instructions. Theprocessor 110 runs the instructions stored in the internal memory 121,to implement various function applications and data processing of themobile phone 100. The internal memory 121 may include a program storagearea and a data storage area. The program storage area may store anoperating system, an application required by at least one function (forexample, a voice playing function or an image playing function), and thelike. The data storage area may store data (such as audio data and aphone book) created during use of the mobile phone 100, and the like. Inaddition, the internal memory 121 may include a high-speed random accessmemory, and may further include a nonvolatile memory, for example, atleast one magnetic disk storage device, a flash memory device, or auniversal flash storage (UFS).

The mobile phone 100 may implement an audio function, for example, musicplaying and recording, through the audio module 170, the speaker 170A,the receiver 170B, the microphone 170C, the headset jack 170D, theapplication processor, and the like.

The audio module 170 is configured to convert digital audio informationinto an analog audio signal for output and is also configured to convertanalog audio input into a digital audio signal. The audio module 170 maybe further configured to code and decode audio signals. In someembodiments, the audio module 170 may be disposed in the processor 110,or some functional modules in the audio module 170 are disposed in theprocessor 110.

The speaker 170A, also referred to as a “horn”, is configured to convertan audio electrical signal into a sound signal. The mobile phone 100 maylisten to music or answer a hands-free call through the speaker 170A. Inthe embodiments, the speaker 170A is further configured to play ahelp-seeking speech.

The receiver 170B, also referred to as an “earpiece”, is configured toconvert an audio electrical signal into a sound signal. When a call isanswered or speech information is received by using the mobile phone100, the receiver 170B may be put close to a human ear to listen to aspeech.

The microphone 170C, also referred to as a “mike” or a “mic”, isconfigured to convert a sound signal into an electrical signal. Whenmaking a call or sending speech information, a user may place the mouthof the user near the microphone 170C to make a sound, to input a soundsignal to the microphone 170C. At least one microphone 170C may bedisposed in the mobile phone 100. In some other embodiments, twomicrophones 170C may be disposed in the mobile phone 100, to collect asound signal and further implement a noise reduction function. In someother embodiments, three, four, or more microphones 170C mayalternatively be disposed in the mobile phone 100, to collect a soundsignal, reduce noise, further identify a sound source, implement adirectional recording function, and the like. In the embodiments, themicrophone 170C may be configured to collect a sound signal, such as amoaning sound, a bumping sound, and a crying sound.

The headset jack 170D is configured to connect to a wired headset. Theheadset jack 170D may be the USB port 130 or may be a 3.5 mm open mobileterminal platform (OMTP) standard interface or a cellulartelecommunications industry association of the USA (CTIA) standardinterface.

The pressure sensor 180A is configured to sense a pressure signal andcan convert the pressure signal into an electrical signal. Thebarometric pressure sensor 180C is configured to measure barometricpressure. The distance sensor 180F is configured to measure a distance.The mobile phone 100 may measure a distance through infrared light or alaser. For example, the optical proximity sensor 180G may include alight-emitting diode (LED) and an optical detector, for example, aphotodiode. The light-emitting diode may be an infrared light-emittingdiode. The mobile phone 100 emits infrared light through thelight-emitting diode. The mobile phone 100 detects infrared reflectedlight from a nearby object by using the photodiode. When sufficientreflected light is detected, it can be determined that there is anobject near the mobile phone 100. The ambient light sensor 180L isconfigured to sense ambient light brightness. The fingerprint sensor180H is configured to collect a fingerprint. The temperature sensor 180Jis configured to detect a temperature. The bone conduction sensor 180Mmay obtain a vibration signal. The magnetic sensor 180D includes a Hallsensor. The mobile phone 100 may detect opening and closing of a flipleather case by using the magnetic sensor 180D.

The gyro sensor 180B may be configured to determine a motion posture ofthe mobile phone 100. In some embodiments, an angular velocity of themobile phone 100 around three axes (namely, x, y, and z axes) may bedetermined by using the gyro sensor 180B.

The acceleration sensor 180E may detect values of acceleration of themobile phone 100 in various directions (usually on three axes). When themobile phone 100 is still, a value and a direction of gravity may bedetected. The acceleration sensor 180E may be further configured toidentify a posture of the mobile phone 100 and is applied to anapplication such as a pedometer.

It may be understood that the gyro sensor 180B is configured to measurethe angular velocity of the mobile phone 100, and the accelerationsensor 180E is configured to measure the acceleration of the mobilephone 100. Motion sensors in the embodiments may include the gyro sensor180B and the acceleration sensor 180E. In other words, in theembodiments, the gyro sensor 180B and the acceleration sensor 180E areused together to collect a motion parameter of the electronic device.For example, in a fall process of a user, the motion parameter collectedby the motion sensor may be used to indicate that the mobile phone 100is weightless, then receives great impact force, and finally keeps stillwithin a specific time period (or a motion amplitude is less than apreset threshold). Usually, the mobile phone 100 is carried by the user.Therefore, the motion parameter of the mobile phone 100 may also beconsidered as a motion parameter of the user carrying the mobile phone100.

For example, the acceleration sensor 180E may be a 3-axis accelerationsensor, a 6-axis acceleration sensor, or a 9-axis acceleration sensor.The gyro sensor 180B may be a 3-axis gyro sensor, a 6-axis gyro sensor,or a 9-axis gyro sensor.

The heart rate sensor 180N is configured to measure a heart rate of theuser. For example, the heart rate sensor 180N may be an optical heartrate sensor. The optical heart rate sensor may measure the heart rate byusing a photoplethysmography method. Briefly, light is used to measure apulse. Blood is red and can reflect red light and absorb green light.The mobile phone or a wearable device detects, by using the opticalheart rate sensor, an amount of blood flowing through a wrist at aspecific time point. At a moment of a heartbeat, the amount of bloodflowing through the wrist increases, and more green light is absorbed.At a heartbeat interval, less green light is absorbed. LED light isemitted to a skin, and light reflected back through skin tissue isreceived by a photosensitive device in the optical heart rate sensor.The LED light flashes hundreds of times per second. The optical heartrate sensor may calculate, based on green light received by thephotosensitive device, a quantity of heartbeats per minute of the user,that is, the heart rate.

The touch sensor 180K is also referred to as a “touch panel”. The touchsensor 180K may be disposed on the display 194, and the touch sensor180K and the display 194 form a touchscreen, which is also referred toas a “touch screen”. The touch sensor 180K is configured to detect atouch operation performed on or near the touch sensor 180K. The touchsensor may transfer the detected touch operation to the applicationprocessor, to determine a type of a touch event. The display 194 mayprovide visual output related to the touch operation. In some otherembodiments, the touch sensor 180K may alternatively be disposed on asurface of the mobile phone 100 and is at a location different from thatof the display 194. In some embodiments, after the mobile phone 100determines that the user falls, the touch sensor 180K may collect anoperation entered by the user on the touchscreen. In response to theoperation, the mobile phone 100 may send the help-seeking information ina manner corresponding to the operation.

The button 190 includes a power button, a volume button, and the like.The button 190 may be a mechanical button or may be a touch button. Themotor 191 may generate a vibration prompt. The motor 191 may beconfigured to provide an incoming call vibration prompt or a touchvibration feedback. The indicator 192 may be an indicator light, may beconfigured to indicate a charging status and a power change, and may beconfigured to indicate a message, a missed call, a notification, and thelike. The SIM card interface 195 is configured to connect to a SIM card.The SIM card may be inserted into the SIM card interface 195 or detachedfrom the SIM card interface 195, to implement contact with or detachmentfrom the mobile phone 100. The mobile phone 100 may support one or N SIMcard interfaces, where N is a positive integer greater than 1.

In some other embodiments, the electronic device may be a wearabledevice. As shown in FIG. 2, a smartwatch 200 is used as an example forthe wearable device. As shown in FIG. 2, the smartwatch 200 includes awatch body and a wrist strap that are connected to each other. The watchbody may include a front housing (not shown in FIG. 2), a processor 201,a memory 202, a display 203 (such as a touchscreen), a rear housing (notshown in FIG. 2), a micro control unit (MCU) 204, a sensor module 205, amicrophone (MIC) 206, a wireless communications module 207, a GPS module209, a speaker 208, an RF circuit 210, a power supply 211, a powermanagement module 212, a receiver 213, and the like. Although not shown,the smartwatch 200 may further include an antenna, a button, anindicator, and the like. A person ordinary skill in the art mayunderstand that a structure of the smartwatch 200 shown in FIG. 2 doesnot impose a limitation on the smartwatch, and the smartwatch mayinclude more or fewer components than those shown in the figure, combinesome components, or have different component arrangements.

The sensor module 205 may include at least a gyro sensor 205A and anacceleration sensor 205B, that is, motion sensors in the embodiments.Additionally, the sensor module 205 may further include a pressuresensor 205C, a barometric pressure sensor 205D, a magnetic sensor 205E,a distance sensor 205F, an optical proximity sensor 205G, a fingerprintsensor 205H, a temperature sensor 205J, a touch sensor 205K, an ambientlight sensor 205L, a bone conduction sensor 205M, a heart rate sensor205N, and the like. The sensor module 205 is connected to the MCU 204and is controlled by the MCU 204.

It should be noted that for functions of the sensors in the sensormodule 205, refer to the descriptions of the sensors in the sensormodule 180 in the foregoing embodiment. Details are not described inthis embodiment.

The memory 202 may be configured to store application program code, forexample, application program code used to perform fall detection byperforming the method in the embodiments. The processor 201 may executethe application program code to implement a function of the smartwatch200 in this embodiment.

The memory 202 may further store a Bluetooth address of the smartwatch200. In addition, the memory 202 may further store information about oneor more emergency contacts that are set by a user in the smartwatch 200,for example, a phone number and an account of an instant communicationapplication (which is also referred to as an instant messagingapplication). The memory 202 may further store a phone number of apublic rescue service, such as an emergency phone number (for example,120) and an alarm phone number (for example, 110). The Bluetooth addressmay be a media access control (MAC) address.

The wireless communications module 207 is configured to supportshort-range data exchange between the smartwatch 200 and variouselectronic devices, for example, the mobile phone 100. In someembodiments, the wireless communications module 207 may be a Bluetoothmodule. In some other embodiments, the wireless communications module207 may be a Wi-Fi module.

The smartwatch 200 may include at least one receiver 213 and at leastone microphone 206. The receiver 213 may also be referred to as an“earpiece”, and may be configured to convert an audio electrical signalinto a sound signal and play the sound signal. The microphone 206 mayalso be referred to as a “mike” or a “mic”, and is configured to converta sound signal into an audio electrical signal. The audio electricalsignal is received by an audio circuit and converted into audio data.The audio circuit may also convert the audio data into an electricalsignal and transmit the electrical signal to the speaker 208. Thespeaker 208 converts the electrical signal into a sound signal foroutput. In the embodiments, the speaker 208 may be further configured toplay a help-seeking speech.

The display 203 may be a touchscreen. The touchscreen includes a displaypanel and a touch panel. The display 203 may be configured to displayinformation entered by the user or information provided for the user,and various menus of the watch. Optionally, the display 203 may beconfigured in a form of an LCD, an OLED, or the like. After detecting atouch operation on or near the touch panel, the touch panel transfersthe touch operation to the processor 201 to determine a type of a touchevent. Subsequently, the processor 201 provides corresponding visualoutput on the display 203 based on the type of the touch event.

The smartwatch 200 further includes the power supply 211 (for example, abattery) that supplies power to each component. Optionally, the powersupply 211 may be logically connected to the processor 201 through thepower management system 212, to implement functions such as chargingmanagement, discharging management, and power consumption managementthrough the power management system 212.

Further, the smartwatch 200 shown in FIG. 2 may further include the RFcircuit 210. The RF circuit 210 may be configured to receive and sendsignals in an information receiving and sending process or a callprocess. The RF circuit 210 may receive downlink information from a basestation, transmit the downlink information to the processor 201 forprocessing, and send uplink data to the base station. Usually, the RFcircuit 210 includes but is not limited to an antenna, at least oneamplifier, a transceiver, a coupler, a low noise amplifier, a duplexer,or the like. In addition, the RF circuit 210 may further communicatewith a network and another mobile device through wireless communication.The wireless communication may use any communications standard orprotocol, including but not limited to a global system for mobilecommunications, a general packet radio service, code division multipleaccess, wideband code division multiple access, long term evolution, anemail, a short message service, or the like.

The smartwatch 200 may further include a positioning module, such as theGPS module 209 shown in FIG. 2. Further, the positioning module mayalternatively be a global navigation satellite system GLONASS module, aBDS module, or the like. The positioning module is configured to obtaingeographical location information of the smartwatch 200. In thisembodiment, after detecting that the user falls, the smartwatch 200 maysend help-seeking information including the geographical locationinformation of the smartwatch 200 to an emergency contact or anemergency center. In this way, the emergency contact or the emergencycenter can quickly and accurately locate the fallen person who needshelp and provide help in a timely manner.

It should be understood that the smartwatch 200 shown in FIG. 2 ismerely an example for a wearable device, and the smartwatch 200 may havemore or fewer components than those shown in the figure, or may combinetwo or more components, or may have different component configurations.The components shown in FIG. 2 may be implemented in hardware includingone or more signal processing and/or application-specific integratedcircuits, software, or a combination of hardware and software.

In the following embodiments, an example in which the electronic deviceis the wearable device 10 (that is, a first wearable device) shown inFIG. 1A is used. The wearable device 10 is worn by a user “a”. In theembodiments, an example in which the wearable device 10 performs falldetection on the user “a” is used to describe the method in theembodiments.

An embodiment provides a fall detection-based help-seeking method. Asshown in FIG. 3, the fall detection-based help-seeking method mayinclude S301 to S305.

S301: The wearable device 10 collects a first motion parameter of theuser “a” by using a motion sensor.

The motion sensor may include an acceleration sensor or a gyro sensor.The acceleration sensor is configured to collect acceleration of motionof the wearable device 10, and the gyro sensor is configured to collectangular velocity of the motion of the wearable device 10. In otherwords, the first motion parameter may include the acceleration and theangular velocity of the motion of the wearable device 10.

It may be understood that because the wearable device 10 is worn by theuser “a”, the motion of the wearable device 10 is generated due tomotion of the user “a”. In this case, the first motion parametercollected by the wearable device 10 may be considered as a motionparameter of the user “a”. The first motion parameter may includeacceleration and angular velocity of the motion of the user “a”.

The motion parameter collected by the motion sensor varies with anaction performed by the user “a”. For example, a motion parametercollected by the motion sensor when the user “a” sleeps is differentfrom a motion parameter collected by the motion sensor when the user “a”falls. Therefore, the wearable device 10 may determine, based on thefirst motion parameter collected by the motion sensor, whether the user“a” falls.

S302: The wearable device 10 determines whether the first motionparameter matches a first preset fall parameter.

Usually, when the user falls, the user sequentially experiences thefollowing conditions: weightlessness and landing. When the user touchesthe ground after falling, the user receives a relatively great impactforce. In this case, a body of the user may be seriously injured, andthe user needs to be aided in a timely manner to effectively reduce arisk of accidental injury or death. Based on this, the first preset fallparameter may be a motion parameter detected by the motion sensor whenthe wearable device 10 is weightless and receives relatively greatimpact force.

In some other cases, if the user is seriously injured after falling, theuser cannot autonomously get up or move within a specific time period(for example, a preset time period), that is, a motion amplitude of theuser within the preset time period is relatively small (for example, themotion amplitude is less than a preset amplitude threshold). In thiscase, the first preset fall parameter may be a motion parameter detectedby the motion sensor when the wearable device 10 is weightless andreceives relatively great impact force, and the motion amplitude withinthe preset time period is less than the preset amplitude threshold.

For example, the first preset fall parameter may be obtained bycollecting statistics on motion parameters collected by motion sensorswhen a large quantity of users fall. The first preset fall parameter maybe preconfigured in the wearable device 10. Alternatively, the firstpreset fall parameter is sent by the server 20 shown in FIG. 1A to thewearable device 10. For example, as shown in FIG. 3, before S301, themethod in this embodiment may further include S300: the server 20 sendsthe first preset fall parameter to the wearable device 10.

It should be noted that, for a method in which the wearable device 10determines whether the first motion parameter matches the first presetfall parameter, refer to a method for determining, during falldetection, whether a motion parameter matches the first preset fallparameter in the conventional technology. Details are not described inthis embodiment. In this embodiment, that the wearable device 10determines whether the first motion parameter matches the first presetfall parameter (namely, S302) is referred to as “first-layer detection”of fall detection.

For example, if the first motion parameter matches the first preset fallparameter, it indicates that the first motion parameter may be a motionparameter collected when the user “a” falls, that is, the user “a” mayfall. In this case, the wearable device 10 may perform S303. If thefirst motion parameter does not match the first preset fall parameter,it indicates that the first motion parameter is not a motion parametercollected when the user “a” falls, that is, the user “a” does not fall.In this case, the wearable device may continue to collect a motionparameter of the user “a”, that is, perform S301.

S303: The wearable device 10 obtains a fall confidence of the firstmotion parameter. The fall confidence is used to represent a probabilitythat the first motion parameter is the motion parameter collected whenthe user “a” falls.

The wearable device 10 may determine the fall confidence based on amatching degree between the first motion parameter and a first presetinterference parameter. Alternatively, the wearable device 10 maydetermine the fall confidence of the first motion parameter by using afirst fall detection model. For a method in which the wearable deviceobtains the fall confidence of the first motion parameter, refer todetailed descriptions in the following embodiments. Details are notdescribed herein.

S304: The wearable device 10 determines whether the fall confidence isgreater than a preset confidence threshold.

A higher fall confidence of the first motion parameter indicates ahigher probability that the first motion parameter is the motionparameter collected when the user “a” falls. A lower fall confidence ofthe first motion parameter indicates a lower probability that the firstmotion parameter is the motion parameter collected when the user “a”falls.

The preset confidence threshold may be preconfigured in the wearabledevice 10. Alternatively, the preset confidence threshold may be set bythe user in the wearable device 10. For example, if a maximum value ofthe fall confidence of the motion parameter is 100, the presetconfidence threshold may be any value such as 90, 85, 80, or 75. If amaximum value of the fall confidence of the motion parameter is 100%,the preset confidence threshold may be any value such as 90%, 85%, 80%,or 75%. If a maximum value of the fall confidence of the motionparameter is 10, the preset confidence threshold may be any value suchas 9, 8.5, 8, or 7.5.

If the fall confidence is greater than the preset confidence threshold,it indicates that there is a relatively high probability that the firstmotion parameter is the motion parameter collected when the user “a”falls. In this case, to enable the fallen user “a” to be aided in atimely manner, the wearable device may perform S305 to send help-seekinginformation.

Optionally, if the fall confidence is greater than the preset confidencethreshold, as shown in FIG. 3, the wearable device 10 may furtherperform S306: send a second message to the server 20. The second messageincludes the first motion parameter and first indication information.The first indication information is used to indicate that the firstmotion parameter is the motion parameter collected when the user falls.As shown in FIG. 3, after receiving the second message, the server 20may perform S307 in response to the second message: update the firstpreset fall parameter by using the first motion parameter.

It may be understood that the server 20 may receive motion parameterssent by a large quantity of electronic devices (such as wearabledevices) after the electronic devices determine that users fall andupdate the first preset fall parameter in the server 20 by using themotion parameters. Then, the server 20 may further send an updated firstpreset fall parameter to a plurality of electronic devices (for example,the wearable device 10) managed by the server 20. For example, theserver 20 may periodically send the updated first preset fall parameterto the plurality of electronic devices (for example, the wearable device10). The wearable device 10 performs fall detection by using the updatedfirst preset fall parameter, so that accuracy of fall detection can beimproved.

Alternatively, if the fall confidence is less than the preset confidencethreshold, it indicates that there is a relatively low probability thatthe first motion parameter is the motion parameter collected when theuser “a” falls. In this case, the wearable device may continue tocollect a motion parameter of the user “a”, that is, perform S301.

S305: The wearable device 10 sends the help-seeking information.

In some embodiments, S305 may be as follows: the wearable device 10automatically calls a preset contact to send the help-seekinginformation.

The preset contact in this embodiment may be an emergency contact or apublic rescue service preset in the wearable device 10. For example, aphone number of the public rescue service may be an emergency phonenumber (for example, 120) or an alarm phone number (for example, 110).The emergency contact may be preset by the user in the wearable device10. For a method for setting the emergency contact by the user, refer torelated descriptions in the following embodiments. Details are notdescribed in this embodiment.

For example, the wearable device 10 is a smartwatch 400 worn by the user“a”. It is assumed that preset emergency contacts in the smartwatch 400include a son of the user “a”, the wife of the user “a,” and anemergency phone number 120. After determining that the fall confidenceis greater than the preset confidence threshold (that is, the user “a”falls), the smartwatch 400 may automatically call any preset contact inthe son of the user “a”, the wife of the user “a”, or the emergencyphone number 120. For example, the smartwatch 400 may display a voicecall interface 402 shown in FIG. 4(b), and call a daughter of the user“a”, to send the help-seeking information.

It may be understood that if the user “a” is not seriously injured afterfalling, the user “a” has an autonomous behavior capability. In thiscase, the user “a” may expect to autonomously select an object forhelp-seeking. For example, when the fall is not serious, the user “a”may prefer to seek help from a family member or a friend rather thandialing the emergency phone number 120.

Based on this case, in some other embodiments, before sending thehelp-seeking information, the wearable device 10 may display a firstinterface including a plurality of contact options. Each contact optioncorresponds to one preset contact. The wearable device 10 may receive aselection operation (for example, a single-tap operation) performed bythe user on any contact option in the first interface and call a presetcontact corresponding to the contact option selected by the user, tosend the help-seeking information.

With reference to the foregoing example, after determining that the fallconfidence is greater than the preset confidence threshold (that is, theuser “a” falls), the smartwatch 400 may display a first interface 401shown in FIG. 4(a). The first interface 401 includes the followingcontact options: “wife”, “son”, “daughter”, and “120”. The smartwatch 40may receive a selection operation performed by the user “a” on thecontact option “daughter”. In response to the selection operationperformed by the user on the contact option “daughter”, the smartwatch400 may display the voice call interface 402 shown in FIG. 4(b), andcall the daughter of the user “a”, to send the help-seeking information.

In some embodiments, after the wearable device 10 calls the presetcontact corresponding to the contact option selected by the user (thatis, requests voice communication with the contact selected by the user),if the voice communication is not answered within a first preset timeperiod (for example, 1 minute, 50 seconds, 30 seconds, or 15 seconds),the wearable device 10 may automatically call another preset contact.For example, if the daughter of the user “a” still does not answer thecall after the smartwatch 400 displays the voice call interface 402shown in FIG. 4(b) for the first preset time period, the smartwatch 400may automatically call the son or the wife of the user “a”, or dial 120,to send the help-seeking information. In some other embodiments, if thevoice communication is not answered within the first preset time period,the wearable device 10 may further automatically send a first message toone or more preset contacts.

In some other embodiments, S305 may be as follows: the wearable device10 automatically sends the first message to the one or more presetcontacts, where the first message includes the help-seeking information.

For example, the first message may be “I fell, come quickly to help me”,“I fell, come quickly to take me to the hospital”, or “I fell, seriouslyinjured, come quickly to help me”. The help-seeking information in thefirst message may be preconfigured in the wearable device 10.Alternatively, the help-seeking information may be set by the user inthe wearable device 10. For a method for setting the help-seekinginformation by the user in the wearable device 10, refer to a method forsetting a customized SMS message for automatic reply by the user in amobile phone. Details are not described in this embodiment.

For example, the wearable device 10 may send the first message to theone or more preset contacts through one or more communicationapplications. The one or more communication applications may becommunication applications installed in the wearable device 10. The oneor more communication applications are applications that are installedin the wearable device 10 and that can communicate with another device(for example, a mobile phone of a preset contact). For example, thecommunication application may be a messaging application, Email,iMessage, WeChat, QQ, or Alipay, or the like.

It should be noted that in this embodiment, the communicationapplication runs in the background of the wearable device 10, so thatwhen the user “a” falls, the wearable device 10 can directly invoke thecommunication application to send the first message to the one or morepreset contacts. Alternatively, the user logged in to the communicationapplication on the wearable device 10, and the wearable device 10 storeslogin information (for example, an account and a login password) of thecommunication application, so that the wearable device 10 can start thecommunication application when determining that the user “a” falls andsend the first message to the one or more preset contacts through thecommunication application after starting the communication application.

It may be understood that currently, a public rescue service hasregistered an official account on each communication application. Inthis embodiment, the wearable device 10 may send the first message to anofficial account of the public rescue service on the communicationapplication through the communication application.

In some embodiments, the wearable device 10 may automatically send thefirst message to the one or more preset contacts in the wearable device10 through any communication application (for example, WeChat). Forexample, the communication application is WeChat. With reference to theforegoing example, after determining that the fall confidence is greaterthan the preset confidence threshold (that is, the user “a” falls), thesmartwatch 400 may automatically send a WeChat message to one or morepreset contacts in the son of the user “a”, the daughter of the user“a”, the wife of the user “a”, or the emergency phone number 120. TheWeChat message includes the help-seeking information.

In some other embodiments, to ensure that the user “a” can be aided in atimely manner after falling, the wearable device 10 may automaticallysend the first message to the one or more preset contacts in thewearable device 10 through a plurality of communication applications(for example, WeChat and Messaging). With reference to the foregoingexample, after determining that the user “a” falls, the smartwatch 400may automatically send a WeChat message and an SMS message to one ormore preset contacts in the son of the user “a”, the daughter of theuser “a”, the wife of the user “a”, or the emergency phone number 120.The WeChat message and the SMS message include the help-seekinginformation.

In some embodiments, to enable a rescuer (for example, a family memberor a friend of the user “a”, or a public rescue service worker) toaccurately find the user “a” in a timely manner, the first message mayfurther include geographical location information of the wearable device10. The wearable device 10 includes a positioning module, for example, aGPS positioning module. The wearable device 10 may obtain thegeographical location information of the wearable device 10 by using thepositioning module.

Further, considering that the first message may not be noticed in atimely manner, when sending the first message, the wearable device 10may further call the preset contact for help-seeking. In this way, notonly the help-seeking of the user “a” can be noticed in a timely manner,but also the rescuer can accurately locate the fallen user “a” based onthe geographical location information in the first message and go tohelp in a timely manner.

Optionally, after sending the first message to the one or more presetcontacts, the wearable device 10 may send first prompt information. Thefirst prompt information is used to prompt the user “a” that thewearable device 10 has sent a help-seeking message. For example, thefirst prompt information may be “already ask the emergency contact(through WeChat, Messaging, or another instant messaging application) tohelp you”.

For example, the wearable device 10 may display the first promptinformation on a display (for example, a touchscreen). For example,after sending the first message to the one or more preset contacts, thewearable device 10 may display first prompt information 506 shown inFIG. 5(d). Alternatively, the wearable device 10 may play speechinformation corresponding to the first prompt information.

It may be understood that, after falling, the user “a” may expect toautonomously select an object for help-seeking. For example, when thefall is not serious, the user “a” may prefer to seek help from a familymember or a friend rather than dialing the emergency phone number 120.Based on this case, in this embodiment, before sending the firstmessage, the wearable device 10 may display a first interface includinga plurality of contact options. Each contact option corresponds to onepreset contact. The wearable device 10 may receive a selection operation(for example, a single-tap operation) performed by the user on one ormore contact options in the first interface, and send, through one ormore communication applications, the first message to one or more presetcontacts corresponding to the one or more contact options selected bythe user. Correspondingly, the plurality of contact options displayed inthe first interface are contact options of preset contacts in the one ormore communication applications.

It should be noted that if the wearable device 10 does not receive aselection operation of the user in the first interface within a secondpreset time period, the wearable device 10 may automatically call anypreset contact, or send the first message to one or more presetcontacts.

For example, the communication application is WeChat. It is assumed thata mobile phone 500 shown in FIG. 5(b) is a mobile phone of the son ofthe user “a”, and a WeChat account of the son of the user “a” is used tolog in to WeChat in the mobile phone 500; and a mobile phone 600 shownin FIG. 5(c) is a mobile phone of the daughter of the user “a”, and aWeChat account of the daughter of the user “a” is used to log in toWeChat in the mobile phone 600. After determining that the user “a”falls, the smartwatch 400 may display a first interface 501 shown inFIG. 5(a). The first interface 501 includes a plurality of contactoptions, such as contact options “wife”, “son”, “daughter”, and “120”.The first interface 501 may further include an “OK” button and a“Cancel” button. The “Cancel” button is used to trigger the smartwatch400 to cancel sending the help-seeking information. The “OK” button isused to trigger the smartwatch 400 to send the first message to acontact corresponding to a contact option selected by the user.

The contact options “son” and “daughter” in FIG. 5(a) are selected bythe user. In response to a tap operation (for example, a single-tapoperation) performed by the user on the “OK” button, the smartwatch 400may send the first message to the son (that is, the mobile phone 500)and the daughter (that is, the mobile phone 600) of the user “a” throughWeChat. After receiving the first message, the mobile phone 500 maydisplay a WeChat chat interface 502 in FIG. 5(b) in response to a useroperation. After receiving the first message, the mobile phone 600 maydisplay a WeChat chat interface 505 in FIG. 5(c) in response to a useroperation. As shown in FIG. 5(b), the WeChat chat interface 502 mayinclude help-seeking information 503 and geographical locationinformation 504. For example, the help-seeking information 503 may be “Ifell, come quickly to take me to the hospital”, and the geographicallocation information 504 may be “intersection of Yanta West Road, YantaDistrict, Xi'an”. After sending the first message, the wearable device10 may further display the first prompt information 506 shown in FIG.5(d), for example, “already ask the emergency contact (through WeChat)to help you”.

Optionally, the geographical location information may be a link of ageographical location of the user “a”. For example, as shown in FIG. 6A,the mobile phone 500 may display a WeChat chat interface 601. The WeChatchat interface 601 includes help-seeking information 602 andgeographical location information 603. The geographical locationinformation 603 is a link of a geographical location of the user “a”. Inresponse to a tap operation (for example, a single-tap operation)performed by a user on the geographical location information 603, themobile phone 500 may invoke a map application (for example, Baidu Map)in the mobile phone 500 to accurately determine the geographicallocation of the user “a” (not shown in the figure).

In some cases, if the user “a” is seriously injured after falling, theuser needs to be aided in a timely manner, to effectively reduce a riskof accidental injury or death. However, after the wearable device 10makes the call or sends the first message for help-seeking, even if therescuer can receive the help-seeking of the user “a” in a timely manner,the rescuer may not be able to aid the user “a” in a timely manner. Inthis case, because an optimal aid time is missed, life of the user “a”may be in danger, or a body of the user “a” may be irreversibly harmed.For example, the rescuer may not be able to aid the user “a” in a timelymanner because the rescuer cannot reach, in a timely manner, a locationat which the user “a” falls.

In some other embodiments, to increase a probability that the user “a”can be aided in a timely manner after the user “a” falls, the wearabledevice 10 may further play a help-seeking speech or an alarm sound. Inthis way, after the user “a” falls, people around can find the fallenuser “a” in a timely manner and aid the user “a” in a timely manner.

For example, the help-seeking speech may be “help, help”, “help me, helpme”, or “an old man fell, please help”. For example, the alarm sound maybe “di di di”, “du du du”, or an alarm sound played when an ambulanceexecutes an aid task. Optionally, the wearable device 10 may play thehelp-seeking speech at a maximum play volume of the wearable device 10(for example, of a speaker of the wearable device).

In some other embodiments, after determining that the user “a” falls,the wearable device 10 may enable a speech control function of thewearable device 10. After the wearable device 10 enables the speechcontrol function, the wearable device may receive speech data sent bythe user and perform a corresponding event. For example, a voiceassistant may be installed in the wearable device 10. Usually, thewearable device 10 may monitor speech data. When speech data (forexample, a wake-up word “xiao E, xiao E”) is detected, whether thespeech data matches a wake-up word may be determined. If the speech datamatches the wake-up word, the wearable device 10 may enable the voiceassistant. However, in this embodiment, after determining that the user“a” falls, the wearable device 10 may enable the voice assistant. Thevoice assistant is an important application of an electronic device (forexample, the wearable device 10). The voice assistant may performintelligent conversation and instant question and answer-basedintelligent interaction with a user. In addition, the voice assistantmay further identify a speech command of the user and enable thewearable device 10 to perform an event corresponding to the speechcommand.

In this embodiment, after determining that the user “a” falls, thewearable device 10 may collect speech data, and perform a speech controlevent corresponding to the speech data (that is, a speech command). Forexample, the wearable device 10 may collect the speech data by using amicrophone, receive a speech command of the user by using the voiceassistant, and enable the wearable device 10 to perform an eventcorresponding to the speech command. In this way, after falling, theuser “a” can control, by using a speech, the wearable device 10 to sendthe help-seeking information. For example, the user “a” may say speechdata “call my son”, “send a WeChat message to tell my daughter that Ifell”, or “dial 120”.

In this embodiment, a preset contact does not need to be preconfiguredor set in the wearable device 10. After falling, the user “a” maycontrol, by using the speech data, the wearable device 10 to send thehelp-seeking information to a contact specified by the user.Alternatively, in this embodiment, a preset contact may be preconfiguredor set in the wearable device 10. In this case, after the user “a”falls, provided that the user “a” sends a preset speech command (thatis, speech data), such as “fall”, “falling”, “help”, or “help me”, thewearable device 10 can send the help-seeking information in any one ofthe foregoing help-seeking manners.

When the user “a” falls, the display (for example, a touchscreen) of thewearable device 10 may fail to work normally due to relatively greatimpact force. In some of the foregoing implementations, if the display(for example, a touchscreen) of the wearable device 10 cannot worknormally, the user “a” cannot control the wearable device 10 to send thehelp-seeking information. However, in this embodiment, after falling,the user “a” can normally control, by using speech data, the wearabledevice 10 to send the help-seeking information.

In some other embodiments, after determining that the user “a” falls(that is, performing 610 shown in FIG. 6B), the wearable device 10 mayplay the help-seeking speech or the alarm sound, and start a timer tocount down (that is, perform 611 shown in FIG. 6B). Timing duration forthe timer to count down is a third preset time period. For example, thethird preset time period may be any time length such as 1 minute, 90seconds, 2 minutes, 3 minutes, or 5 minutes.

If the user “a” is not seriously injured after falling, and canautonomously seek medical care, the user “a” may control the wearabledevice 10 to stop playing the help-seeking speech or the alarm sound.For example, the user may control, within the third preset time period(that is, before the countdown ends), the wearable device 10 to stopplaying the help-seeking speech or the alarm sound.

For example, if the wearable device 10 receives a first operation (thatis, performing 612 or 613 shown in FIG. 6B) of the user within the thirdpreset time period, in response to the first operation, the wearabledevice 10 may stop playing the help-seeking speech or the alarm sound(that is, perform 615 shown in FIG. 6B). For example, the firstoperation may be a touch operation or a gesture, such as an S-shapedgesture, entered by the user on the display (for example, a touchscreen)of the wearable device 10. For another example, the first operation maybe a first tap operation, such as a double-tap operation, performed bythe user on the wearable device 10. For still another example, afterdetermining that the user “a” falls, the wearable device 10 may enablethe speech control function of the wearable device 10. The firstoperation is speech data (that is, a speech command), for example, “stopplaying”, sent by the user for controlling the wearable device 10 tostop playing the alarm sound or the help-seeking speech.

Alternatively, if the user “a” is seriously injured after falling, andcannot autonomously seek medical care, but still has a behaviorcapability, the user “a” may control the wearable device 10 to send thehelp-seeking information in a help-seeking manner selected by the user.

For example, if the wearable device 10 receives a second operation (thatis, performing 612 or 613 shown in FIG. 6B) of the user within the thirdpreset time period, in response to the second operation, the wearabledevice 10 may call a preset contact or send the first message to apreset contact (that is, perform 616 shown in FIG. 6B). The secondoperation is different from the first operation. For example, the secondoperation may be a touch operation or a gesture, such as a √-shapedgesture, entered by the user on the display (for example, a touchscreen)of the wearable device 10. For another example, the second operation maybe a second tap operation, such as a triple-tap operation, performed bythe user on the wearable device 10. For still another example, afterdetermining that the user “a” falls, the wearable device 10 may enablethe speech control function of the wearable device 10. The secondoperation is that the user sends speech data such as “call my son”,“send a WeChat message to tell my daughter that I fell”, or “dial 120”.

Alternatively, if the user “a” is seriously injured after falling, andcannot operate the wearable device 10, the wearable device 10 mayautomatically use any one of the foregoing help-seeking manners afterthe third preset time period (that is, after the countdown ends), orseek help with reference to at least two help-seeking manners (that is,perform 616 shown in FIG. 6B).

It should be noted that in this embodiment, a manner in which thewearable device 10 sends the help-seeking information (that is, ahelp-seeking manner) includes, but is not limited to, the foregoingmanners. The wearable device 10 may seek help in any one of theforegoing help-seeking manners, or with reference to at least twohelp-seeking manners. The manner in which the wearable device 10 sendsthe help-seeking information is not limited in this embodiment.

In this embodiment, after determining that the first motion parametermatches the first preset fall parameter (that is, determining that theuser may fall), the wearable device 10 may further determine whether thefall confidence is greater than the preset confidence threshold. Inother words, the wearable device 10 may determine, through doubledetection, whether the user falls. In this way, accuracy of falldetection performed by the wearable device 10 can be improved, and aprobability of mistakenly triggering automatic help-seeking of thewearable device 10 can be reduced.

In some embodiments, the wearable device 10 may determine the fallconfidence of the first motion parameter based on the matching degreebetween the first motion parameter and the first preset interferenceparameter. As shown in FIG. 7, S303 shown in FIG. 3 may be replaced withS701, and S304 may be replaced with S702.

S701: The wearable device 10 obtains the matching degree between thefirst motion parameter and the first preset interference parameter anddetermines the fall confidence of the first motion parameter based onthe matching degree.

The first preset interference parameter is a motion parameter collectedwhen the user performs a preset interference action. The presetinterference action may be slapping a table, waving a hand, goingdownstairs, sitting down, lying down, nodding, shaking a head, kicking,running, jumping, and the like by the user. When the user performs thepreset interference action, the motion sensor of the wearable device 10may collect the first preset interference parameter.

In this embodiment, a lower matching degree between the first motionparameter and the first preset interference parameter indicates a higherfall confidence. A higher matching degree between the first motionparameter and the first preset interference parameter indicates a lowerfall confidence.

For example, a sum of the matching degree a and the fall confidence b isa fixed value. For example, a+b=m. Herein, m is any value, such as 1, 2,or 3. For example, m=1. When the matching degree a=30%, the fallconfidence b=1 −30%=70%. The preset confidence threshold may be anyvalue such as 90%, 85%, 80%, or 75%.

For another example, the matching degree a is inversely proportional tothe fall confidence b. For example, a×b=n. Herein, n is any value, suchas 1, 2, 10, or 50. For example, n=10. When the matching degree a=20,the fall confidence b=n/a=10/20=1/2=50%. The preset confidence thresholdmay be 90%, 85%, 80%, 75%, or the like.

In this embodiment, that the wearable device 10 determines whether thefall confidence is greater than the preset confidence threshold (thatis, S304) may also be considered that the wearable device 10 performsS702.

S702: The wearable device 10 determines whether the matching degreebetween the first motion parameter and the first preset interferenceparameter is less than a specific value.

It may be understood that a lower matching degree indicates a higherfall confidence, and a higher matching degree indicates a lower fallconfidence. Therefore, if the fall confidence is greater than the presetconfidence threshold (that is, the fall confidence is relatively high),the matching degree is relatively low (for example, less than thespecific value). When the matching degree is less than the specificvalue, it indicates that there is a relatively low probability that thefirst motion parameter is the first preset interference parameter, andthere is a relatively high probability that the motion parameter is theparameter collected when the user “a” falls, that is, there is arelatively high probability that the user “a” falls. In this case, thewearable device 10 may send the help-seeking information (that is,perform S305). It should be noted that in this embodiment, S701 and S702performed by the wearable device 10 is referred to as “second-layerdetection” of fall detection. Through the “second-layer detection”, thewearable device 10 can exclude mistaken triggering caused by the presetinterference action on automatic help-seeking of the wearable device 10.

For example, the first preset interference parameter may be obtained bycollecting statistics on motion parameters collected by motion sensorswhen a large quantity of users perform the preset interference action.The first preset interference parameter may be preconfigured in thewearable device 10. Alternatively, the first preset interferenceparameter is sent by the server 20 shown in FIG. 1A to the wearabledevice 10. For example, as shown in FIG. 7, the method in thisembodiment may further include S700: the server 20 sends the firstpreset interference parameter to the wearable device 10.

Optionally, if the fall confidence is less than or equal to the presetconfidence threshold (or the matching degree between the first motionparameter and the first preset interference parameter is greater than orequal to the specific value), it indicates that the user “a” does notfall, and the first motion parameter is the first preset interferenceparameter. In this case, as shown in FIG. 7, the wearable device 10 mayperform S703: send a third message to the server 20. The third messageincludes the first motion parameter and second indication information.The second indication information is used to indicate that the firstmotion parameter is not the motion parameter collected when the userfalls. As shown in FIG. 7, after receiving the third message, the server20 may perform S704 in response to the third message: update the firstpreset interference parameter by using the first motion parameter.

It may be understood that the server 20 may receive motion parameterssent by a large quantity of electronic devices (such as wearabledevices) after the electronic devices determine that users do not falland update the first preset interference parameter in the server 20 byusing the motion parameters. Then, the server 20 may further send anupdated first preset interference parameter to a plurality of electronicdevices (for example, the wearable device 10) managed by the server 20.For example, the server 20 may periodically send the updated firstpreset interference parameter to the plurality of electronic devices(for example, the wearable device 10). The wearable device 10 performsfall detection by using the updated first preset interference parameter,so that accuracy of fall detection can be improved.

In this embodiment, after determining that the first motion parametermatches the first preset fall parameter (that is, performing the“first-layer detection”), the wearable device 10 may further determinewhether the first motion parameter is the motion parameter collectedwhen the user performs the preset interference action (that is, performthe “second-layer detection”), to exclude mistaken triggering caused bythe preset interference action on automatic help-seeking of the wearabledevice 10. In other words, the wearable device 10 may determine, throughdouble detection, that is, the “first-layer detection” and the“second-layer detection”, whether the user falls. In this way, accuracyof fall detection performed by the wearable device 10 can be improved,and a probability of mistakenly triggering automatic help-seeking of thewearable device 10 can be reduced.

In some other embodiments, the wearable device 10 may store model codeof one or more fall detection models. The one or more fall detectionmodels include the first fall detection model. The first fall detectionmodel is used to determine a fall confidence of a motion parameter (forexample, the first motion parameter). The first fall detection model isan artificial intelligence (AI) model obtained by performing sampletraining by using a plurality of second motion parameters.Alternatively, the first fall detection model is an AI model obtained byperforming sample training by using a plurality of second motionparameters and a plurality of third motion parameters.

The plurality of second motion parameters are motion parameterscollected when a plurality of users fall. The plurality of third motionparameters are motion parameters collected when the plurality of usersperform the foregoing preset interference action.

The first fall detection model may be sent by the server 20 shown inFIG. 1A to the wearable device 10. The first fall detection model may bean AI model obtained by the server 20 by performing sample training onthe plurality of second motion parameters (or the plurality of secondmotion parameters and the plurality of third motion parameters) by usinga deep learning algorithm. For a specific method in which the server 20performs sample training to obtain the first fall detection model, referto a model training method in the conventional technology. Details arenot described in this embodiment.

In this embodiment, the wearable device 10 may determine the fallconfidence of the first motion parameter by using the first falldetection model. As shown in FIG. 8, S303 shown in FIG. 3 may bereplaced with S801.

S801: The wearable device 10 runs the model code of the first falldetection model, to determine the fall confidence of the first motionparameter.

The first fall detection model obtained through sample training has acapability of determining a fall confidence of a motion parameter.Therefore, the wearable device 10 may run the model code of the firstfall detection model, and use the first motion parameter as input, toobtain the fall confidence of the first motion parameter.

It may be understood that the first fall detection model is an AI modelthat is obtained through training with a large quantity of samples andthat has a capability of determining a fall confidence of a motionparameter. Therefore, compared with a fall confidence obtained bycomparing or performing matching on the first motion parameter and apreset parameter (for example, the first preset interference parameter),the fall confidence determined by running the model code of the firstfall detection model is more accurate.

It should be noted that in this embodiment, S801 and S304 performed bythe wearable device 10 are referred to as “third-layer detection” offall detection. Compared with the “second-layer detection”, the“third-layer detection” is more accurate, and therefore, accuracy offall detection can be improved.

Optionally, after S304, if the fall confidence of the first motionparameter is greater than the preset confidence threshold, the wearabledevice 10 may perform S306 to send the first motion parameter and thefirst indication information to the server 20. After receiving the firstmotion parameter and the first indication information, in response tothe first indication information, the server 20 may perform S307 toupdate the first preset fall parameter by using the first motionparameter and may further perform S802: update the first fall detectionmodel by using the first motion parameter as a fall parameter. That theserver 20 updates the first fall detection model by using the firstmotion parameter as a fall parameter means that the server performsmodel training by using the first motion parameter as a training sample,so that the first fall detection model can learn of a capability ofdetermining that the first motion parameter is motion data collectedwhen the user falls.

If the fall confidence of the first motion parameter is less than orequal to the preset confidence threshold, the wearable device 10 mayperform S702 to send the first motion parameter and the secondindication information to the server 20. After receiving the firstmotion parameter and the second indication information, in response tothe second indication information, the server 20 may perform S803:update the first fall detection model by using the first motionparameter as an interference parameter. That the server 20 updates thefirst fall detection model by using the first motion parameter as aninterference parameter means that the server 20 performs model trainingby using the first motion parameter as a training sample, so that thefirst fall detection model can learn of a capability of recognizing thatthe first motion parameter is not motion data collected when the userfalls.

It may be understood that after updating the first fall detection model,the server 20 may generate model code of an updated first fall detectionmodel. Then, the server 20 may send the model code of the updated firstfall detection model to a plurality of electronic devices (for example,the wearable device 10) managed by the server 20. For example, theserver 20 may periodically send the model code of the updated first falldetection model to the plurality of electronic devices (for example, thewearable device 10). The wearable device 10 replaces the model code thatis stored in the wearable device 10 and that is of the first falldetection model with the model code of the updated first fall detectionmodel. The wearable device 10 performs fall detection by using theupdated first fall detection model, so that accuracy of fall detectioncan be improved.

In this embodiment, the wearable device 10 may determine, through doubledetection, that is, the “first-layer detection” and the “third-layerdetection”, whether the user falls. In this way, accuracy of falldetection performed by the wearable device 10 can be improved, and aprobability of mistakenly triggering automatic help-seeking of thewearable device 10 can be reduced.

To further improve accuracy of fall detection performed by the wearabledevice 10, in some other embodiments, the wearable device 10 maydetermine, through triple detection, that is, the “first-layerdetection”, the “second-layer detection”, and the “third-layerdetection”, whether the user falls. For example, as shown in FIG. 9A andFIG. 9B, before S801 shown in FIG. 8, the method in this embodiment mayfurther include S901.

S901: The wearable device 10 determines that the first motion parameteris not the first preset interference parameter.

The wearable device 10 may determine whether a matching degree betweenthe first motion parameter and the first preset interference parameteris less than a specific value. If the matching degree between the firstmotion parameter and the first preset interference parameter is lessthan the specific value, the wearable device 10 may determine that thefirst motion parameter is not the first preset interference parameter.For a specific method in which the wearable device 10 determines whetherthe matching degree between the first motion parameter and the firstpreset interference parameter is less than the specific value, refer tothe detailed descriptions in S702. Details are not described in thisembodiment.

In this embodiment, after determining that the first motion parametermatches the first preset fall parameter (that is, performing the“first-layer detection”), the wearable device 10 may determine whetherthe first motion parameter is the motion parameter collected when theuser performs the preset interference action (that is, perform the“second-layer detection”), to exclude mistaken triggering caused by thepreset interference action on automatic help-seeking of the wearabledevice 10, and then perform the “third-layer detection” by using thefall detection model. In other words, the wearable device 10 maydetermine, through triple detection, that is, the “first-layerdetection”, the “second-layer detection”, and the “third-layerdetection”, whether the user falls. In this way, accuracy of falldetection performed by the wearable device 10 can be improved, and aprobability of mistakenly triggering automatic help-seeking of thewearable device 10 can be reduced.

In some other embodiments, after S304, even if the fall confidence isless than or equal to the preset confidence threshold, the wearabledevice 10 does not immediately perform S301, but determines whether theuser “a” makes a preset moaning sound or crying sound, and whether aheart rate of the user “a” is abnormal. For example, after S304, if thefall confidence is less than or equal to the preset confidencethreshold, the method in this embodiment may further include S1001 toS1003. Before S1001, the method in this embodiment may further includeS1000.

For example, with reference to FIG. 3, and as shown in FIG. 10A, afterS304, if the fall confidence is less than or equal to the presetconfidence threshold, the method in this embodiment may further includeS1001 to S1003.

S1001: The wearable device 10 determines collected speech data and heartrate information collected by a heart rate sensor.

For example, the heart rate information of the user “a” may be aquantity of heartbeats per minute of the user “a”. As shown in FIG. 10A,before S1001, the method in this embodiment may further include S1000:the wearable device 10 collects the speech data by using a microphone,and collects the heart rate information of the user “a” by using theheart rate sensor.

S1002: The wearable device 10 determines that the microphone does notcollect a preset moaning sound, bumping sound, or crying sound, and theheart rate information of the user “a” indicates that the heart rate ofthe user “a” is normal.

That the heart rate information of the user “a” indicates that the heartrate of the user “a” is normal is as follows: the heart rate informationof the user “a” indicates that the quantity of heartbeats per minute ofthe user “a” falls within a value interval (m1, m2). Herein, m1 is afirst quantity of heartbeats, and m2 is a second quantity of heartbeats.m2 is greater than m1. The first quantity m1 of heartbeats is a minimumquantity of heartbeats per minute of a normal person, and the secondquantity m2 of heartbeats is a maximum quantity of heartbeats per minuteof a normal person. For example, m1=60, and m2=100.

S1003: The wearable device 10 determines that the microphone collects apreset moaning sound, bumping sound, or crying sound, or the heart rateinformation of the user “a” indicates that the heart rate of the user“a” is abnormal.

That the heart rate information of the user “a” indicates that the heartrate of the user “a” is abnormal is as follows: the heart rateinformation of the user “a” indicates that the quantity of heartbeatsper minute of the user “a” is less than the first quantity of heartbeatsor greater than the second quantity of heartbeats.

For example, as shown in FIG. 10A, if the microphone does not collectthe preset moaning sound or crying sound, and the heart rate informationof the user “a” indicates that the heart rate of the user “a” is normal(that is, S1002), it indicates that the user “a” does not fall, and thewearable device 10 may perform S301 and S1000. If the microphonecollects the preset moaning sound or crying sound, or the heart rateinformation of the user “a” indicates that the heart rate of the user“a” is abnormal (that is, S1003), it indicates that the user “a” falls,and the wearable device 10 may perform S305.

In this embodiment, the wearable device 10 may further determine, basedon whether the user “a” makes the preset moaning sound or crying soundand whether the heart rate of the user “a” is normal, whether the user“a” falls. This can improve accuracy of fall detection performed by thewearable device 10.

In some other embodiments, the foregoing sensor module may furtherinclude a sound sensor (that is, the microphone) and the heart ratesensor. The wearable device 10 may collect the speech data by using thesound sensor (that is, the microphone), and collect the heart rateinformation by using the heart rate sensor. The first preset fallparameter may further include the preset moaning sound, bumping sound,or crying sound, the first quantity of heartbeats, and the secondquantity of heartbeats. In the foregoing “first-layer detection”, if thewearable device 10 determines that the first motion parameter matchesthe first preset fall parameter, the wearable device 10 may furtherdetermine “whether the speech data collected by the microphone includesthe preset moaning sound, bumping sound, or crying sound” and “whetherthe heart rate indicated by the heart rate information falls within thevalue interval (m1, m2)”. If the speech data includes the preset moaningsound, bumping sound, or crying sound, and the heart rate falls withinthe value interval (m1, m2), the wearable device 10 may perform the“second-layer detection” or the “third-layer detection”. Detection ofthe speech data and the heart rate is added to the “first-layerdetection”, so that accuracy of performing the “first-layer detection”by the wearable device 10 can be improved.

When the user wears different types of wearable devices, the wearabledevices are worn at different locations. For example, a watch-typewearable device (such as a smartwatch) supported by a wrist is worn on awrist of the user. For another example, a glass-type wearable device(such as smart glasses) supported by a head is worn on a head of theuser. For still another example, a shoes-type wearable device (such as asmart anklet) supported by a foot is worn on an ankle of the user.

It may be understood that, when the user falls, wearable devices worn atdifferent locations detect different motion parameters. In addition, thewearable devices worn at the different locations detect differentinterference parameters. Therefore, in this embodiment, for each type ofwearable devices (for example, watch-type wearable devices), the server20 may collect statistics on motion parameters collected by this type ofwearable devices when a large quantity of users wearing this type ofwearable devices fall, to obtain a preset fall parameter correspondingto this type of wearable devices. For each type of wearable devices, theserver 20 may collect statistics on motion parameters collected by thistype of wearable devices when a large quantity of users wearing thistype of wearable devices perform a preset interference action, to obtaina preset interference parameter corresponding to this type of wearabledevices.

In this embodiment, the server 20 may store a group of a preset fallparameter and a preset interference parameter for each type of wearabledevices. For example, a preset fall parameter library 1010 shown in FIG.10B may be a storage area for storing the preset fall parameter in theserver 20. As shown in FIG. 10B, the preset fall parameter library 1010stores a preset fall parameter 1011 corresponding to the watch-typewearable device, a preset fall parameter 1012 corresponding to theglass-type wearable device, a preset fall parameter 1013 correspondingto the shoes-type wearable device, and the like. For another example, apreset interference parameter library 1020 shown in FIG. 10B may be astorage area for storing the preset interference parameter in the server20. As shown in FIG. 10B, the preset interference parameter library 1020stores a preset interference parameter 1021 corresponding to thewatch-type wearable device, a preset interference parameter 1022corresponding to the glass-type wearable device, a preset interferenceparameter 1023 corresponding to the shoes-type wearable device, and thelike.

Preset interference actions may be different for different types ofwearable devices. For example, a preset interference actioncorresponding to the watch-type wearable device may include an actionwith a relatively large arm motion amplitude, such as slapping a table,waving a hand, swinging an arm, and wearing clothes by a user. Foranother example, a preset interference action corresponding to theglass-type wearable device may include an action with a relatively largehead motion amplitude, such as nodding, shaking a head, lying down, andjumping. As still another example, a preset interference actioncorresponding to the shoes-type wearable device may include an actionwith a relatively large leg motion amplitude, such as kicking, running,and jumping. Therefore, for each type of wearable devices, the server 20may collect statistics on motion parameters collected by this type ofwearable devices when a large quantity of users wearing this type ofwearable devices (for example, watch-type wearable devices) perform apreset interference action corresponding to this type of wearabledevices, to obtain a preset interference parameter corresponding to thistype of wearable devices.

Likewise, for each type of wearable devices, the server 20 may collectstatistics on motion parameters (that is, a plurality of second motionparameters) collected by this type of wearable devices when a largequantity of users wearing this type of wearable devices fall, andcollect statistics on motion parameters (that is, a plurality of thirdmotion parameters) collected by this type of wearable devices when alarge quantity of users wearing this type of wearable devices perform apreset interference action. Then, for each type of wearable devices, theserver 20 may perform sample training on the plurality of second motionparameters (or the plurality of second motion parameters and theplurality of third motion parameters) collected by this type of wearabledevices, to obtain a fall detection model corresponding to this type ofwearable devices and generate corresponding model code.

The server 20 may further store model code of a fall detection model foreach type of wearable devices. For example, a fall detection modellibrary 1030 shown in FIG. 10B may be a storage area for storing themodel code of the fall detection model in the server 20. As shown inFIG. 10B, the fall detection model library 1030 stores model code 1031of a fall detection model corresponding to the watch-type wearabledevice, model code 1032 of a fall detection model corresponding to theglass-type wearable device, model code 1033 of a fall detection modelcorresponding to the shoes-type wearable device, and the like.

It should be noted that the wearable device 10 may store a preset fallparameter (that is, the first preset fall parameter), a presetinterference parameter (that is, the first preset interferenceparameter), and model code of a fall detection model (that is, the firstfall detection model) that correspond to a type of only the wearabledevice 10. For example, it is assumed that the wearable device 10 is theglass-type wearable device. The wearable device 10 may store the presetfall parameter 1012, the preset interference parameter 1022, and themodel code 1032 of the fall detection model that are shown in FIG. 10B.

In this embodiment, the server 20 may store a group of a preset fallparameter, a preset interference parameter, and a fall detection modelfor each type of wearable devices. Therefore, the server 20 may update,based on a first identifier by using the first motion parameter, apreset fall parameter or a preset interference parameter and a falldetection model that correspond to the first identifier. For example,the second message and the third message may further include a firstidentifier of the wearable device 10. The first identifier may be usedto indicate the type of the wearable device 10. For example, when thefirst identifier is 00, it indicates that the wearable device 10 is thewatch-type wearable device. When the first identifier is 01, itindicates that the wearable device 10 is the glass-type wearable device.When the first identifier is 10, it indicates that the wearable device10 is the shoes-type wearable device.

For example, the first identifier indicates that the wearable device 10is the shoes-type wearable device. It is assumed that the wearabledevice 10 further sends the first indication information when sendingthe first motion parameter to the server 20. In response to the firstindication information, the server 20 may update a preset fall parameterand a fall detection model. For example, the first identifier indicatesthat the wearable device 10 is the shoes-type wearable device.Therefore, the server 20 may update, by using the first motionparameter, the preset fall parameter 1013 and the fall detection model1033 that correspond to the shoes-type wearable device.

Likewise, the server 20 may send an updated preset fall parameter, anupdated preset interference parameter, and model code of an updated falldetection model that correspond to the first identifier to the wearabledevice 10 based on the first identifier of the wearable device 10.

In this embodiment, the server 20 may maintain a group of a preset fallparameter, a preset interference parameter, and a fall detection modelfor each of different types of wearable devices. In addition, thewearable device 10 may store a group of a preset fall parameter, apreset interference parameter, and a fall detection model thatcorrespond to the type of the wearable device 10. The wearable device 10performs fall detection by using the group of the preset fall parameter,the preset interference parameter, and the fall detection model thatcorrespond to the type of the wearable device 10. This can improveaccuracy of the fall detection.

When the user is in different scenarios, granularities for performingfall detection by the wearable device 10 are different. A granularity atwhich the wearable device 10 performs fall detection may be representedby using a preset fall parameter, a preset interference parameter, and afall detection model. In other words, in this embodiment, when the useris in different scenarios, different preset fall parameters, differentpreset interference parameters, and different fall detection models areused by the wearable device 10 to perform fall detection.

For example, the foregoing scenarios may include at least a sleepingscenario, an outdoor scenario, an indoor scenario, a sport scenario, ascenario of going upstairs and downstairs, and the like. In thisembodiment, the wearable device 10 may determine a scenario in which theuser is located by using parameters collected by a plurality of sensors(for example, a heart rate sensor, an acceleration sensor, and a gyrosensor), the positioning module (for example, a GPS module), and thelike of the wearable device 10. In the following embodiment, a scenario1, a scenario 2, and a scenario 3 are used as examples to describe themethod in this embodiment. The scenario 1, the scenario 2, and thescenario 3 are three scenarios in a sleeping scenario, an outdoorscenario, an indoor scenario, a sport scenario, a scenario of goingupstairs and downstairs, and the like.

It may be understood that when the user falls in different scenarios,motion parameters detected by the wearable device are different. Inaddition, interference parameters collected when the user falls indifferent scenarios are also different. Therefore, in this embodiment,for each scenario, the server 20 may collect statistics on motionparameters collected by wearable devices when a large quantity of usersfall in the scenario, to obtain a preset fall parameter corresponding tothe scenario. For each scenario, the server 20 may collect statistics onmotion parameters collected by wearable devices when a large quantity ofusers perform a preset interference action in the scenario, to obtain apreset interference parameter corresponding to the scenario.

In this embodiment, the server 20 and the wearable device 10 may store agroup of a preset fall parameter and a preset interference parameter foreach scenario. For example, a preset fall parameter library 1110 shownin FIG. 11 may be a storage area for storing the preset fall parameterin the server 20 and the wearable device 10. As shown in FIG. 11, apreset fall parameter library 1110 stores a preset fall parameter 1111of the scenario 1, a preset fall parameter 1112 of the scenario 2, apreset fall parameter 1113 of the scenario 3, and the like. For anotherexample, a preset interference parameter library 1120 shown in FIG. 11may be a storage area for storing the preset interference parameter inthe server 20 and the wearable device 10. As shown in FIG. 11, a presetinterference parameter library 1120 stores a preset interferenceparameter 1121 of the scenario 1, a preset interference parameter 1122of the scenario 2, a preset interference parameter 1123 of the scenario3, and the like.

Preset interference actions of the user in different scenarios may bedifferent. For example, preset interference actions of the user in asleeping scenario may include turning over and the like. For anotherexample, interference actions of the user in a sport scenario mayinclude kicking, running, jumping, and the like. For still anotherexample, interference actions of the user in a scenario of goingupstairs and downstairs may include lifting a leg and the like.Therefore, for each scenario, the server 20 may collect statistics onmotion parameters collected by wearable devices when a large quantity ofusers perform a preset interference action corresponding to the scenarioin the scenario, to obtain a preset interference parameter correspondingto the scenario.

Likewise, for each scenario, the server 20 may collect statistics onmotion parameters (that is, a plurality of second motion parameters)collected by wearable devices when a large quantity of users fall in thescenario, and collect statistics on motion parameters (that is, aplurality of third motion parameters) collected by wearable devices whena large quantity of users perform a preset interference action in thescenario. Then, for each scenario, the server 20 may perform sampletraining on the plurality of second motion parameters (or the pluralityof second motion parameters and the plurality of third motionparameters) collected by the wearable devices in the scenario, to obtaina fall detection model corresponding to the scenario and generatecorresponding model code.

The server 20 and the wearable device 10 may further store model code ofa fall detection model for each scenario. For example, a fall detectionmodel library 1130 shown in FIG. 11 may be a storage area for storingthe model code of the fall detection model in the server 20 and thewearable device 10. As shown in FIG. 11, the fall detection modellibrary 1130 stores model code 1131 of a fall detection model in thescenario 1, model code 1132 of a fall detection model in the scenario 2,model code 1133 of a fall detection model in the scenario 3, and thelike.

In this embodiment, the server 20 and the wearable device 10 may store agroup of a preset fall parameter, a preset interference parameter, and afall detection model for each scenario. Therefore, the server 20 mayupdate, based on a second identifier (that is, an identifier of ascenario) by using the first motion parameter, a preset fall parameteror a preset interference parameter and a fall detection model thatcorrespond to the second identifier. For example, the second message andthe third message may further include the second identifier, that is, anidentifier of a scenario in which the user “a” is currently located. Forexample, when the second identifier is 000, it indicates that the user“a” is in a sleeping scenario. When the identifier is 001, it indicatesthat the user “a” is in an outdoor scenario. When the identifier is 010,it indicates that the user “a” is in a sport scenario.

For example, the second identifier indicates that the user “a” is in thescenario 3 (for example, a sport scenario). It is assumed that thewearable device 10 further sends the first indication information whensending the first motion parameter to the server 20. In response to thefirst indication information, the server 20 may update a preset fallparameter and a fall detection model. For example, the second identifierindicates that the user “a” is in the scenario 3. Therefore, the server20 may update the preset fall parameter 1113 and the fall detectionmodel 1133 in the scenario 3 by using the first motion parameter.

Likewise, the server 20 may send, to the wearable device 10, an updatedpreset fall parameter, an updated preset interference parameter, andmodel code of an updated fall detection model in the scenario indicatedby the second identifier.

In this embodiment, the server 20 and the wearable device 10 maymaintain a group of a preset fall parameter, a preset interferenceparameter, and a fall detection model for each of different scenarios.When the user is in different scenarios, the wearable device 10 performsfall detection by using groups of preset fall parameters, presetinterference parameters, and fall detection models that correspond tothe scenarios. This can improve accuracy of the fall detection.

Optionally, preset fall parameters in each scenario may be classifiedinto a plurality of types of preset fall parameters based on types ofwearable devices. For example, as shown in FIG. 12, the preset fallparameter 1111 in the scenario 1 may include a preset fall parameter1111 a corresponding to the watch-type wearable device, a preset fallparameter 1111 b corresponding to the glass-type wearable device, apreset fall parameter 1111 c corresponding to the shoes-type wearabledevice, and the like.

Preset interference parameters in each scenario may be classified into aplurality of types of preset interference parameters based on types ofwearable devices. For example, as shown in FIG. 12, the presetinterference parameter 1121 in the scenario 1 may include a presetinterference parameter 1121 a corresponding to the watch-type wearabledevice, a preset interference parameter 1121 b corresponding to theglass-type wearable device, a preset interference parameter 1121 ccorresponding to the shoes-type wearable device, and the like.

Fall detection modes in each scenario may be classified into a pluralityof types of fall detection models based on types of wearable devices.For example, as shown in FIG. 12, the model code 1131 of the falldetection model in the scenario 1 may include model code 1131 a of afall detection model corresponding to the watch-type wearable device,model code 1131 b of a fall detection model corresponding to theglass-type wearable device, model code 1131 c of a fall detection modelcorresponding to the shoes-type wearable device, and the like.

The preset contact in this embodiment may be preset by the user in thewearable device 10. When the wearable device 10 is powered on for thefirst time, the wearable device 10 may guide the user to set anemergency contact. For example, after being powered on for the firsttime, the wearable device 10 may display an emergency contact settinginterface. Alternatively, a settings application of the wearable device10 may include an “emergency contact” option. For example, a settinginterface 1301 shown in FIG. 13(a) includes an “emergency contact”option 1302. In response to a tap operation performed by the user on the“emergency contact” option, the wearable device 10 may display anemergency contact setting interface 1303 shown in FIG. 13(b). Inresponse to information that is about an emergency contact and that isentered by the user in the emergency contact setting interface, thewearable device 10 stores the information that is about the emergencycontact and that is entered by the user.

For example, the emergency contact setting interface 1303 includes acontact information input box 1305, and the contact information inputbox 1305 is used to enter information about an emergency contact. Thewearable device 10 may receive a mobile phone number or a contact nameentered by the user in the input box 1305, and then obtain and storeinformation about the contact from an address book of the wearabledevice 10. Alternatively, the wearable device 10 may display, forselection by the user, a list of contacts in an address book of thewearable device 10 in response to a tap operation performed by the useron a contact adding button 1306.

It should be noted that specific content and an interface form of theemergency contact setting interface in this embodiment include but arenot limited to the emergency contact setting interface 1303 shown inFIG. 13(b), and another interface form of the emergency contact settinginterface is not described in this embodiment.

In this embodiment, the wearable device 10 may receive and store theemergency contact that is set by the user. In this way, even if the userfalls, the wearable device 10 may still perform the method in thisembodiment, to request the emergency contact preset by the user in thewearable device 10 to aid the user.

Some embodiments provide an electronic device, and the electronic devicemay include a motion sensor. The motion sensor includes an accelerationsensor or a gyro sensor. The electronic device further includes a memoryand one or more processors. The motion sensor, the memory, and theprocessor are coupled. The memory is configured to store computerprogram code, and the computer program code includes computerinstructions. When the processor executes the computer instructions, theelectronic device may perform functions or steps performed by theelectronic device in the foregoing method embodiments. For a structureof the electronic device, refer to the structure of the mobile phone 100shown in FIG. 1B or the structure of the smartwatch 200 shown in FIG. 2.

An embodiment further provides a chip system. As shown in FIG. 14, thechip system includes at least one processor 1401 and at least oneinterface circuit 1402. The processor 1401 and the interface circuit1402 may be interconnected by using a line. For example, the interfacecircuit 1402 may be configured to receive a signal from anotherapparatus (for example, a memory of an electronic device). For anotherexample, the interface circuit 1402 may be configured to send a signalto another apparatus (for example, the processor 1401 or a touchscreenof an electronic device). For example, the interface circuit 1402 mayread instructions stored in the memory and send the instructions to theprocessor 1401. When the instructions are executed by the processor1401, the electronic device is enabled to perform the steps in theforegoing embodiments. Additionally, the chip system may further includeanother discrete device. This is not limited in this embodiment.

An embodiment further provides a computer storage medium. The computerstorage medium includes computer instructions. When the computerinstructions are run on the foregoing electronic device, the electronicdevice is enabled to perform functions or steps performed by theelectronic device in the foregoing method embodiments.

An embodiment further provides a computer program product. When thecomputer program product runs on a computer, the computer is enabled toperform functions or steps performed by the electronic device in theforegoing method embodiments.

The foregoing descriptions about implementations allow a person ofordinary skill in the art to clearly understand that, for the purpose ofconvenient and brief description, division into only the foregoingfunctional modules is used as an example for illustration. In actualapplication, the foregoing functions can be allocated to differentfunctional modules for implementation based on a requirement, that is,an inner structure of an apparatus is divided into different functionalmodules to implement all or some of the functions described above. For adetailed working process of the foregoing system, apparatus, and unit,refer to a corresponding process in the foregoing method embodiments.Details are not described herein again.

In the several embodiments, it should be understood that the disclosedsystem, apparatus, and method may be implemented in other manners. Forexample, the described apparatus embodiments are merely examples. Forexample, division into the modules or units is merely logical functiondivision. There may be another division manner in actual implementation.For example, a plurality of units or components may be combined orintegrated into another system, or some features may be ignored or notperformed. In addition, the displayed or discussed mutual couplings ordirect couplings or communication connections may be implemented throughsome interfaces. The indirect couplings or communication connectionsbetween the apparatuses or units may be implemented in electronic,mechanical, or other forms.

The units described as separate parts may or may not be physicallyseparate, and parts displayed as units may or may not be physical units,may be located in one place, or may be distributed on a plurality ofnetwork units. Some or all of the units may be selected based on anactual requirement to achieve the objectives of the solutions of theembodiments.

In addition, functional units in the embodiments may be integrated intoone processing unit, or each of the units may exist alone physically, ortwo or more units may be integrated into one unit. The integrated unitmay be implemented in a form of hardware or may be implemented in a formof a software functional unit.

When the integrated unit is implemented in a form of a softwarefunctional unit and sold or used as an independent product, theintegrated unit may be stored in a computer-readable storage medium.Based on such an understanding, the solutions of the embodimentsessentially, or the part contributing to the prior art, or all or someof the solutions may be implemented in a form of a software product. Thecomputer software product is stored in a storage medium and includesseveral instructions for instructing a computer device (which may be apersonal computer, a server, or a network device) or a processor toperform all or some of the steps of the methods described in theembodiments. The foregoing storage medium includes: any medium that canstore program code, such as a flash memory, a removable hard disk, aread-only memory, a random access memory, a magnetic disk, or a compactdisc.

The foregoing descriptions are merely specific implementations of theembodiments, and are not intended to limit the scope of the embodiments.Any variation or replacement within the scope disclosed in theembodiments shall fall within the scope of the embodiments.

What is claimed is:
 1. A help-seeking method applied to an electronicdevice comprising a motion sensor having at least one of an accelerationsensor or a gyro sensor, the help-seeking method comprising: collecting,by the electronic device, a first motion parameter of a user by usingthe motion sensor; obtaining, by the electronic device, a fallconfidence of the first motion parameter by comparing the first motionparameter to a first preset fall parameter, wherein the fall confidenceof the first motion parameter is used to represent a probability thatthe first motion parameter is a motion parameter collected when the userfalls; and sending, by the electronic device, help-seeking informationafter the fall confidence of the first motion parameter is greater thana preset confidence threshold.
 2. The help-seeking method according toclaim 1, wherein the sending, by the electronic device, of thehelp-seeking information further comprises: playing, by the electronicdevice, at least one of a help-seeking speech or an alarm sound.
 3. Thehelp-seeking method according to claim 1, wherein the sending, by theelectronic device, of the help-seeking information further comprises:calling, by the electronic device, a first preset contact, wherein thefirst preset contact is any emergency contact or a public rescue servicepreset in the electronic device.
 4. The help-seeking method according toclaim 3, wherein the sending, by the electronic device, of thehelp-seeking information further comprises: displaying, by theelectronic device, a first interface, wherein the first interfacecomprises a plurality of contact options, each contact optioncorresponds to a respective preset contact in the electronic device, andthe respective preset contact comprises the emergency contact or thepublic rescue service preset in the electronic device; and receiving, bythe electronic device, a selection operation performed by the user on acontact option of the first preset contact in the first interface; andthe calling, by the electronic device, of the first preset contactfurther comprises: calling, by the electronic device, the first presetcontact in response to the selection operation performed by the user onthe contact option of the first preset contact.
 5. The help-seekingmethod according to claim 1, wherein the sending, by the electronicdevice, of the help-seeking information further comprises: sending, bythe electronic device, a first message to one or more preset contactsthrough one or more communication applications, wherein the firstmessage comprises the help-seeking information, and the one or morepreset contacts comprise the emergency contact or the public rescueservice preset in the electronic device.
 6. The help-seeking methodaccording to claim 5, wherein the electronic device comprises apositioning module, and the help-seeking method further comprises:obtaining, by the electronic device, geographical location informationof the electronic device by using the positioning module, wherein thefirst message further comprises the geographical location information.7. The help-seeking method according to claim 1, wherein the sending, bythe electronic device, of the help-seeking information furthercomprises: collecting, by the electronic device, speech data of theuser; and, in response to the speech data, performing a speech controlevent corresponding to the speech data; and sending the help-seekinginformation.
 8. The help-seeking method according to claim 1, whereinthe electronic device stores a model code of a first fall detectionmodel, the first fall detection model is used to determine a fallconfidence of a motion parameter, and the first fall detection model iseither an artificial intelligence (AI) model obtained by performingsample training by using a plurality of second motion parameters, or thefirst fall detection model is an AI model obtained by performing sampletraining by using the plurality of second motion parameters and aplurality of third motion parameters; and the obtaining, by theelectronic device, of the fall confidence of the first motion parameterfurther comprises: running, by the electronic device, the model code ofthe first fall detection model, to determine the fall confidence of thefirst motion parameter, wherein the plurality of second motionparameters are motion parameters collected when a plurality of usersfall, and the plurality of third motion parameters are motion parameterscollected when the plurality of users perform a preset interferenceaction.
 9. The help-seeking method according to claim 1, wherein theobtaining, by the electronic device, of the fall confidence of the firstmotion parameter further comprises: obtaining, by the electronic device,a matching degree between the first motion parameter and a first presetinterference parameter; and determining the fall confidence based on thematching degree, wherein a lower matching degree indicates a higher fallconfidence and a higher matching degree indicates a lower fallconfidence.
 10. The help-seeking method according to claim 1, whereinthe electronic device further comprises a heart rate sensor and amicrophone, and the help-seeking method further comprises: collecting,by the electronic device, heart rate information of the user by usingthe heart rate sensor; collecting speech data of the user by using themicrophone; after the fall confidence of the first motion parameter isless than or equal to the preset confidence threshold, determining, bythe electronic device, that the microphone collects a preset moaningsound, a bumping sound, or a crying sound, or the heart rate informationindicates that a heart rate of the user is less than a first quantity ofheart beats or greater than a second quantity of heart beats, whereinthe first quantity of heart beats is a minimum quantity of heart beatsper minute of a normal person, and the second quantity of heart beats isa maximum quantity of heart beats per minute of the normal person; andsending, by the electronic device, the help-seeking information.
 11. Anelectronic device comprising a motion sensor having at least one of anacceleration sensor or a gyro sensor; a memory; and one or moreprocessors, wherein the motion sensor, the memory, and the processor arecoupled, the memory is configured to store computer program code, thecomputer program code comprises computer instructions, and when thecomputer instructions are executed by the electronic device, theelectronic device is configured to collect a first motion parameter of auser, obtain a fall confidence of the first motion parameter bycomparing the first motion parameter collected by the motion sensor to afirst preset fall parameter, wherein the fall confidence of the firstmotion parameter is used to represent a probability that the firstmotion parameter is a motion parameter collected when the user falls,and send help-seeking information after the fall confidence of the firstmotion parameter is greater than a preset confidence threshold.
 12. Theelectronic device according to claim 11, further comprising: a speaker,wherein the electronic device is further configured to control thespeaker to play at least one of a help-seeking speech or an alarm sound.13. The electronic device according to claim 11, wherein the electronicdevice is further configured to call a first preset contact, wherein thefirst preset contact is any emergency contact or a public rescue servicepre-stored in the memory.
 14. The electronic device according to claim13, further comprising: a display having a touchscreen, wherein theelectronic device is further configured to control the display todisplay a first interface, wherein the first interface comprises aplurality of contact options, each contact option corresponds to arespective preset contact in the electronic device, and the respectivepreset contact comprises the emergency contact or the public rescueservice preset in the electronic device, receive a selection operationperformed by the user on a contact option of the first preset contact inthe first interface, and call the first preset contact in response tothe selection operation performed by the user on the contact option ofthe first preset contact.
 15. The electronic device according to claim11, wherein one or more communication applications are installed in theelectronic device, and the electronic device is further configured tosend a first message to one or more preset contacts through the one ormore communication applications, wherein the first message comprises thehelp-seeking information, and the one or more preset contacts comprisethe emergency contact or the public rescue service pre-stored in thememory.
 16. The electronic device according to claim 15, furthercomprising: a positioning module, wherein the electronic device isfurther configured to obtain geographical location information of theelectronic device, wherein the first message further comprises thegeographical location information.
 17. The electronic device accordingto claim 11, further comprising: a microphone, wherein the electronicdevice further configured to collect speech data of the user, and inresponse to the speech data, perform a speech control eventcorresponding to the speech data, and send the help-seeking information.18. The electronic device according to claim 11, wherein the electronicdevice is further configured to obtain a matching degree between thefirst motion parameter and a first preset interference parameter, anddetermine the fall confidence based on the matching degree, wherein alower matching degree indicates a higher fall confidence, and a highermatching degree indicates a lower fall confidence.
 19. The electronicdevice according to claim 11, further comprising: a heart rate sensor;and a microphone, wherein the electronic device is further configured tocollect heart rate information of the user, collect speech data of theuser, and, after the fall confidence of the first motion parameter isless than or equal to the preset confidence threshold, determine thatthe microphone collects a preset moaning sound, a bumping sound, or acrying sound, or the heart rate information indicates that a heart rateof the user is less than a first quantity of heart beats or greater thana second quantity of heart beats, wherein the first quantity of heartbeats is a minimum quantity of heart beats per minute of a normalperson, and the second quantity of heart beats is a maximum quantity ofheart beats per minute of the normal person; and send the help-seekinginformation.
 20. A chip system, wherein the chip system is used in anelectronic device comprising a touchscreen, the chip system comprises:one or more interface circuits; and one or more processors, wherein aninterface circuit and the one or more processors are interconnectedthrough a line, the one or more interface circuits are configured toreceive a signal from a memory of the electronic device, and send thesignal to the processor, the signal comprises computer instructionsstored in the memory, and when the processor executes the computerinstructions, the electronic device is configured to collect a firstmotion parameter of a user, obtain a fall confidence of the first motionparameter by comparing the first motion parameter collected by a motionsensor to a first preset fall parameter, wherein the fall confidence ofthe first motion parameter is used to represent a probability that thefirst motion parameter is a motion parameter collected when the userfalls, and send help-seeking information after the fall confidence ofthe first motion parameter is greater than a preset confidencethreshold.